Matthew Wright Headshot

Matthew Wright

Department Chair

Department of Computing Security
Golisano College of Computing and Information Sciences
matthew.wright@rit.edu
585-475-5432
Office Location
GCI-3783
Office Mailing Address
152 Lomb Memorial Drive Rochester, NY 14623

Matthew Wright

Department Chair

Department of Computing Security
Golisano College of Computing and Information Sciences

Education

BS, Harvey Mudd College; MS, Ph.D., University of Massachusetts at Amherst

Bio

Matt Wright is the Director of Research for the Global Cybersecurity Institute and a Professor of Computing Security. He graduated with his PhD from the Department of Computer Science at the University of Massachusetts in May, 2005, where he earned his MS in 2002. His dissertation work examined attacks and defenses of systems that provide anonymity online. His other interests include adversarial machine learning and understanding the human element of security. Previously, he earned his BS degree in Computer Science at Harvey Mudd College. He has been the lead investigator on over $3.7 million in funded projects, including an NSF CAREER award, and he has published over 100 peer-reviewed papers, including numerous contributions in the most prestigious venues focused on computer security and privacy. Learn more: https://sites.google.com/site/matthewkwright/

matthew.wright@rit.edu
585-475-5432
Personal Links
Personal Website
Google Scholar
Twitter
LinkedIn
Areas of Expertise
Anonymity and Privacy Online
Adversarial Machine Learning
Artificial Intelligence
Distributed Systems
Human-Computer Interaction
Security and Privacy

Currently Teaching

CSEC-490
Capstone in Computing Security
3 Credits
This is a capstone course for students in the information security and forensics program. Students will apply knowledge and skills learned and work on real world projects in various areas of computing security. Projects may require performing security analysis of systems, networks, and software, etc., devising and implementing security solutions in real world applications.
CSEC-520
Cyber Analytics and Machine Learning
3 Credits
The course provides students an opportunity to explore methods and applications in cyber analytics with advanced machine learning algorithms including deep learning. Students will learn how to use machine learning methods to solve cybersecurity problems such as network security, anomaly detection, malware analysis, etc. Students will also learn basic concepts and algorithms in machine learning such as clustering, neural networks, adversarial machine learning, etc. Students taking this course should have the 4th year status and completed MATH-190 Discrete Math, MATH-251 Probability and Statistics I, and MATH-241 Linear Algebra.
CSEC-620
Cyber Analytics and Machine Learning
3 Credits
The course provides students an opportunity to explore methods and applications in cyber analytics with advanced machine learning algorithms including deep learning. Students will learn how to use machine learning methods to solve cybersecurity problems such as network security, anomaly detection, malware analysis, etc. Students will also learn basic concepts and algorithms in machine learning such as clustering, neural networks, adversarial machine learning, etc. A key component of the course will be an independent exploratory project to solve a security program with machine learning algorithms. Students taking this course should have knowledge in Discrete Math, Probability and Statistics, and Linear Algebra. Students should also be able to program in Python.
CSEC-720
Deep Learning Security
3 Credits
This course covers the intersection of cybersecurity and deep learning technologies such as CNNs, LSTMs, and GANs. Topics include the application of deep learning to traffic analysis, Deepfake detection, malware classification, fooling deep learning classifiers with adversarial examples, network attack prediction and modeling, poisoning attacks, and privacy attacks like model inversion and membership inference. Students will present research papers, perform several exercises to apply attack and defense techniques, and complete a final research project. Prior experience with machine learning concepts and implementation is required, but necessary details on deep learning will be covered.

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