SAGE: Stealthy Attack GEneration for Cyber-Physical Systems

TL;DR

This paper introduces SAGE, a framework for generating stealthy insider attacks on cyber-physical systems by optimizing damage, detectability, and cost, using minimal perturbations to evade detection.

Contribution

The paper formulates a novel optimization-based approach for creating stealthy insider attacks on CPS considering physical constraints and multiple objectives.

Findings

SAGE can cause significant damage while remaining undetected.

The attacks are low-cost and effective against various anomaly detection algorithms.

The framework aids in designing resilient CPS and detection methods.

Abstract

Cyber-physical systems (CPS) have been increasingly attacked by hackers. Recent studies have shown that CPS are especially vulnerable to insider attacks, in which case the attacker has full knowledge of the systems configuration. To better prevent such types of attacks, we need to understand how insider attacks are generated. Typically, there are three critical aspects for a successful insider attack: (i) Maximize damage, (ii) Avoid detection and (iii) Minimize the attack cost. In this paper we propose a Stealthy Attack GEneration (SAGE) framework by formulizing a novel optimization problem considering these three objectives and the physical constraints of the CPS. By adding small worst-case perturbations to the system, the SAGE attack can generate significant damage, while remaining undetected by the systems monitoring algorithms. The proposed methodology is evaluated on several anomaly detection algorithms. The results show that SAGE attacks can cause severe damage while staying undetected and keeping the cost of an attack low. Our method can be accessed in the supplementary material of this paper to aid researcher and practitioners in the design and development of resilient CPS and detection algorithms.