Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning
This work addresses security assessment for Industrial Control Systems by automating attack generation, though it is incremental as it builds on existing association rule mining methods.
The paper tackled the problem of generating diverse attack patterns for testing anomaly detection in Industrial Control Systems, resulting in over 300,000 new attack vectors from a Water Treatment plant dataset.
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 300,000 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.