Active Attack Detection and Control in Constrained Cyber-Physical Systems Under Prevented Actuation Attack

TL;DR

This paper introduces an active detection and control scheme for constrained cyber-physical systems to identify and mitigate prevented actuation attacks by designing control inputs that enhance attack detection.

Contribution

It presents a novel combined detection and control framework using multiple-model adaptive estimation and probabilistic optimization for attack detection in constrained systems.

Findings

Effective detection of prevented actuation attacks demonstrated in simulations.

Control inputs optimized to improve attack detection accuracy.

Framework applicable to real-world cyber-physical systems like irrigation channels.

Abstract

This paper proposes an active attack detection scheme for constrained cyber-physical systems. Despite passive approaches where the detection is based on the analysis of the input-output data, active approaches interact with the system by designing the control input so to improve detection. This paper focuses on the prevented actuation attack, where the attacker prevents the exchange of information between the controller and actuators. The proposed scheme consists of two units: 1) detection, and 2) control. The detection unit includes a set of parallel detectors, which are designed based on the multiple-model adaptive estimation approach to detect the attack and to identify the attacked actuator(s). For what regards the control unit, a constrained optimization approach is developed to determine the control input such that the control and detection aims are achieved. In the formulation of the detection and control objective functions, a probabilistic approach is used to reap the benefits of the \textit{a priori} information availability. The effectiveness of the proposed scheme is demonstrated through a simulation study on an irrigation channel.