Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System

TL;DR

This paper introduces a novel method for automatically modeling cyber-physical systems by applying supervised machine learning to data traces from mutated software components, enabling early attack detection.

Contribution

It presents a new approach that uses code mutation and machine learning to automatically learn invariants of CPS for security monitoring.

Findings

Successfully learned physical invariants of a water purification system

Detected 85% of simulated network and code attacks

Automated framework for mutation, data collection, and model learning

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.