Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System
2018 IEEE Symposium on Security and Privacy (SP) (2018)
San Fransisco, CA, US
May 21, 2018 to May 23, 2018
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SP.2018.00016
Yuqi Chen , Singapore University of Technology and Design
Christopher M. Poskitt , Singapore University of Technology and Design
Jun Sun , Singapore University of Technology and Design
Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the efficacy of this approach on the simulator of a real-world water purification plant, presenting a framework that automatically generates mutants, collects data traces, and learns an SVM-based model. Using cross-validation and statistical model checking, we show that the learnt model characterises an invariant physical property of the system. Furthermore, we demonstrate the usefulness of the invariant by subjecting the system to 55 network and code-modification attacks, and showing that it can detect 85% of them from the data logs generated at runtime.
cyber-physical-systems, water-treatment-systems, invariants, anomaly-detection, attestation, system-modelling, machine-learning, mutation-testing, attacks
Y. Chen, C. M. Poskitt and J. Sun, "Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System," 2018 IEEE Symposium on Security and Privacy (SP), San Fransisco, CA, US, , pp. 240-252.