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.

Over-actuated systems, namely systems with more inputs than outputs, can increase control performance, yet are susceptible to model-based undetectable attacks if the actuator channel is compromised. In this paper, we show how implementing a sparse actuator schedule can introduce security by rendering these attacks ineffective. We formulate a novel methodology whereby a periodic sparse schedule, implemented at the actuators, secures the system by ensuring that a malicious adversary cannot exploit actuator redundancy to deploy undetectable attacks. The schedule is designed offline and repeats periodically at the actuators, so that the adversary is constrained to only tamper with the active actuators. We devise a degeneracyaware greedy selection procedure with low computational complexity to design an actuator schedule that renders undetectable attacks ineffective, whilst simultaneously providing relatively small performance degradation. We illustrate the effectiveness of our approach using a reference tracking model predictive controller on the IEEE-39 bus power network employing the designed sparse schedule.