Attack Detection Through Time Fingerprinting: A Stochastic Event-Triggered Control Approach

Abstract

We propose a novel cyber-attack detection scheme for control schemes regulated via Stochastic Event-Triggered Control, to detect packets that are maliciously injected by an adversary. The diagnosis scheme relies on assessing whether the arrival time of the information packets received from the controller are compatible with the nominal probability distribution of triggering, or whether they are anomalous. To contrast the threat of an eavesdropping adversary capable of estimating the nominal triggering distribution, we propose a switching scheme, whereby the probability of triggering is drawn among a set of stochastic triggering mechanisms, which is such that the reconstruction of the communication pattern by an eavesdropper becomes computationally infeasible. We design the set of stochastic triggering mechanisms via the solution of an optimization problem, which embeds an explicit trade-off between the properties of the nominal Stochastic Event-Triggered Controller and the detection scheme. The results are illustrated through a numerical example.