Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2018

Abstract

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.

Keywords

anomaly detection, attacks, attestation, cyber physical systems, invariants, machine learning, mutation testing, system modelling, water treatment systems

Discipline

Information Security | Software Engineering

Research Areas

Cybersecurity; Software and Cyber-Physical Systems

Publication

2018 39th IEEE Symposium on Security and Privacy (S&P 2018): San Francisco, May 21-23: Proceedings

First Page

648

Last Page

660

ISBN

9781538643525

Identifier

10.1109/SP.2018.00016

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/SP.2018.00016

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