Cyber-physical systems (CPSs) automating critical public infrastructure face a pervasive threat of attack, motivating research into different types of countermeasures. Assessing the effectiveness of these countermeasures is challenging, however, as benchmarks are difficult to construct manually, existing automated testing solutions often make unrealistic assumptions, and blindly fuzzing is ineffective at finding attacks due to the enormous search spaces and resource requirements. In this work, we propose active sensor fuzzing, a fully automated approach for building test suites without requiring any a prior knowledge about a CPS. Our approach employs active learning techniques. Applied to a real-world water treatment system, our approach manages to find attacks that drive the system into 15 different unsafe states involving water flow, pressure, and tank levels, including nine that were not covered by an established attack benchmark. Furthermore, we successfully generate targeted multi-point attacks which have been long suspected to be possible. We reveal that active sensor fuzzing successfully extends the attack benchmarks generated by our previous work, an ML-guided fuzzing tool, with two more kinds of attacks. Finally, we investigate the impact of active learning on models and the reason that the model trained with active learning is able to discover more attacks.
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