Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning
Published in ACM CPSIoTSec 2021, 2021
Recommended citation: Muhammad Azmi Umer, Chuadhry Mujeeb Ahmed, Muhammad Taha Jilani, and Aditya P. Mathur. 2021. Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning. In Proceedings of the 2th Workshop on CPS&IoT Security and Privacy (CPSIoTSec 21). Association for Computing Machinery, New York, NY, USA, 35–40. https://doi.org/10.1145/3462633.3483976 https://dl.acm.org/doi/abs/10.1145/3462633.3483976
Adversarial learning is used to test the robustness of machine learning algorithms under attack and create attacks that deceive the anomaly detection methods in Industrial Control System (ICS). Given that security assessment of an ICS demands that an exhaustive set of possible attack patterns is studied, in this work, we propose an association rule mining-based attack generation technique. The technique has been implemented using data from a Secure Water Treatment plant. The proposed technique was able to generate more than 110,000 attack patterns constituting a vast majority of new attack vectors which were not seen before. Automatically generated attacks improve our understanding of the potential attacks and enable the design of robust attack detection techniques.