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.

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