Limiting The Impact of Stealthy Attacks on Industrial Control Systems
David Urbina, Alvaro Cardenas, Niles O. Tippenhauer, Junia Valente, Mustafa Faisal, Justin Ruths, Richard Candell, Heinrik Sandberg
While attacks on information systems have for most practical purposes binary outcomes information was manipulated/eavesdropped, or not), attacks manipulating the sensor or control signals of Industrial Control Systems (ICS) can be tuned by the attacker to cause a continuous spectrum in damages. Attackers that want to remain undetected can attempt to hide their manipulation of the system by following closely the expected behavior of the system, while injecting just enough false information at each time step to achieve their goals. In this work, we show and compare attack-detection schemes that can limit the impact of such stealthy attacks. We start with a comprehensive review of related work on attack detection schemes in the security and control systems community. We then show that many of those works use detection schemes that are not limiting the impact of stealthy attacks. We propose a new metric to measure the impact of stealthy attacks and how they relate to our selection on an upper bound on false alarms. We finally show that the impact of such attacks can be mitigated in several cases by the proper combination and configuration of detection schemes. We demonstrate the effectiveness of our algorithms through simulations and experiments using real ICS testbeds and real ICS systems.
Proceeedings of the 23rd ACM Conference on Computer and Communications Security
October 24-28, 2016
23rd ACM Conference on Computer and Communications Security