Cyberattack Forecasting Using Predictive Information

Synopsis

Patent US 11,632,386 B2 describes a system and method for detecting and classifying anomalies in power consumption within a data center, with a particular focus on identifying cryptocurrency mining operations. The invention addresses the growing challenge of unauthorized or inefficient power usage in data centers, which can lead to increased operational costs, reduced equipment lifespan, and potential security risks.
A key novel aspect of this patent is its multi-faceted approach to anomaly detection. It employs a combination of power supply unit (PSU) data analysis, environmental sensor data (such as temperature), and network traffic analysis to create a comprehensive profile of normal and anomalous power consumption. Specifically, the system utilizes machine learning models, including a Variational Autoencoder (VAE) and a Long Short-Term Memory (LSTM) network, to learn the typical power consumption patterns of various components within the data center. The VAE identifies anomalous PSU behavior by comparing real-time data to learned normal patterns, while the LSTM model processes sequential data from PSUs to detect deviations over time. This allows for the identification of subtle, yet significant, power usage patterns indicative of activities like cryptocurrency mining, which often involve high, continuous power draws that deviate from standard server workloads.
Furthermore, the invention incorporates features such as a hierarchical data collection system, starting from individual PSU sensors and aggregating data up to rack and row levels. This allows for both granular and holistic anomaly detection. The system can also differentiate between expected power fluctuations (e.g., due to software updates or maintenance) and true anomalies through its learning algorithms. The inclusion of temperature sensors helps in correlating power spikes with heat generation, a common byproduct of intensive computing activities.
The commercial potential of this invention is significant for any organization operating data centers, regardless of scale. As energy costs continue to rise and data center efficiency becomes a critical metric, the ability to precisely monitor and control power consumption offers substantial financial benefits.

Possible applications include:
Cost Optimization: Identifying and mitigating unauthorized or inefficient power usage directly reduces electricity bills, leading to substantial operational savings for data center operators.
Resource Allocation and Planning: By understanding real-time power consumption patterns, data center managers can optimize resource allocation, prevent overloading, and make more informed decisions regarding capacity planning and infrastructure upgrades.
Security and Compliance: Unauthorized activities like cryptocurrency mining can pose security risks, including malware propagation and intellectual property theft. This system provides a tool for early detection of such illicit activities, enhancing overall data center security.
Preventative Maintenance: Anomalous power signatures can sometimes indicate impending hardware failures. Early detection of these patterns can enable proactive maintenance, preventing costly downtime and extending the lifespan of valuable equipment.
Carbon Footprint Reduction: Optimized power usage directly translates to a reduced carbon footprint, supporting sustainability initiatives and compliance with environmental regulations.

This technology offers a robust and intelligent solution for data center management, providing operators with unprecedented visibility and control over their power infrastructure, ultimately leading to more efficient, secure, and cost-effective operations.