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Advanced Vulnerability Discovery and AI

This module explores the intersection of vulnerability discovery and Artificial Intelligence (AI), focusing on how AI techniques can automate and enhance the identification of security vulnerabilities in software and systems. It covers the use of machine learning models to predict and locate potential security flaws, the training of AI on historical vulnerability data, and the ethical considerations of automated testing and exploitation.

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Chio, Clarence, and David Freeman. Machine Learning and Security: Protecting Systems with Data and Algorithms. First edition. Sebastopol, CA: O’Reilly Media, 2018.

Wang, Bolun, Yuanshun Yao, Bimal Viswanath, Haitao Zheng, and Ben Y. Zhao. “With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning.” In Proceedings of the 27th USENIX Conference on Security Symposium, 1281–97. SEC’18. USA: USENIX Association, 2018.

Anderson, Hyrum S., Jonathan Woodbridge, and Bobby Filar. “DeepDGA: Adversarially-Tuned Domain Generation and Detection.” arXiv, 2016.

https://doi.org/10.48550/ARXIV.1610.01969

Shirazi, Hossein, Bruhadeshwar Bezawada, Indrakshi Ray, and Charles Anderson. “Adversarial Sampling Attacks Against Phishing Detection.” In Data and Applications Security and Privacy XXXIII, edited by Simon N. Foley, 11559:83–101. Cham: Springer International Publishing, 2019.

https://doi.org/10.1007/978-3-030-22479-0_5

Erba, Alessandro, Riccardo Taormina, Stefano Galelli, Marcello Pogliani, Michele Carminati, Stefano Zanero, and Nils Ole Tippenhauer. “Constrained Concealment Attacks against Reconstruction-Based Anomaly Detectors in Industrial Control Systems.” In Annual Computer Security Applications Conference, 480–95, 2020.

https://doi.org/10.1145/3427228.3427660

Kuleshov, Volodymyr, Shantanu Thakoor, Tingfung Lau, and Stefano Ermon. “Adversarial Examples for Natural Language Classification Problems,” February 15, 2018.

https://openreview.net/forum?id=r1QZ3zbAZ

Demetrio, Luca, Battista Biggio, Giovanni Lagorio, Fabio Roli, and Alessandro Armando. “Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries.” arXiv, 2019.

https://doi.org/10.48550/ARXIV.1901.03583

Kuppa, Aditya, Slawomir Grzonkowski, Muhammad Rizwan Asghar, and Nhien-An Le-Khac. “Black Box Attacks on Deep Anomaly Detectors.” In Proceedings of the 14th International Conference on Availability, Reliability and Security, 1–10. Canterbury CA United Kingdom: ACM, 2019.

https://doi.org/10.1145/3339252.3339266

Gibert, Daniel, Carles Mateu, and Jordi Planes. “The Rise of Machine Learning for Detection and Classification of Malware: Research Developments, Trends and Challenges.” Journal of Network and Computer Applications 153 (2020): 102526.

https://doi.org/10.1016/j.jnca.2019.102526

Rosenberg, Ihai, Asaf Shabtai, Yuval Elovici, and Lior Rokach. “Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain.” arXiv, March 13, 2021.

https://doi.org/10.48550/arXiv.2007.02407

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Chio, Clarence, and David Freeman. Machine Learning and Security: Protecting Systems with Data and Algorithms. First edition. Sebastopol, CA: O’Reilly Media, 2018.

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