Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System
This addresses the challenge of manual model-building for CPS security, offering an automated solution for detecting attacks in critical infrastructure.
The paper tackles the problem of automatically constructing accurate models for cyber-physical systems to detect attacks, by using supervised machine learning on data from software mutants, and demonstrates on a water purification plant simulator that it can detect 85% of 55 attacks.
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.