Biblio

This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex tasks that are time-sensitive, in the presence of an adversary. The task is specified as a formula in metric interval temporal logic (MITL). The adversary is assumed to have the ability to tamper with the control input to the CPS and also manipulate timing information perceived by the CPS. In order to model the interaction between the CPS and the adversary, and also the effect of these two classes of attacks, we define an entity called a durational stochastic game (DSG). DSGs probabilistically capture transitions between states in the environment, and also the time taken for these transitions. With the policy of the defender represented as a finite state controller (FSC), we present a value-iteration based algorithm that computes an FSC that maximizes the probability of satisfying the MITL specification under the two classes of attacks. A numerical case-study on a signalized traffic network is presented to illustrate our results.

The cybersecurity of the modern control system is becoming a critical issue to the cyber-physical systems (CPS). Mitigating potential cyberattacks in the control system is an important concern in the controller design to enhance the resilience of the overall system. This paper presents a novel robust control architecture for the PV converter system to mitigate the sensor and actuator attack and reduce the influence of the system uncertainty. The sensor and actuator attack is a vicious attack scenario when the attack signals are injected into the sensor and actuator in a CPS simultaneously. A p-synthesis robust control architecture is proposed to mitigate the sensor and actuator attack and limit the system uncertainty perturbations in a DC-DC photovoltaic (PV) converter. A new system state matrix and control architecture is presented by integrating the original system state, injected attack signals and system uncertainty perturbations. In the case study, the proposed μ-synthesis robust controller exhibits a robust performance in the face of the sensor and actuator attack.

This paper addresses the problem of state estimation of a linear time-invariant system when some of the sensors or/and actuators are under adversarial attack. In our set-up, the adversarial agent attacks a sensor (actuator) by manipulating its measurement (input), and we impose no constraint on how the measurements (inputs) are corrupted. We introduce the notion of ``sparse strong observability'' to characterize systems for which the state estimation is possible, given bounds on the number of attacked sensors and actuators. Furthermore, we develop a secure state estimator based on Satisfiability Modulo Theory (SMT) solvers.

In this paper, we propose a novel adaptive control architecture for addressing security and safety in cyber-physical systems subject to exogenous disturbances. Specifically, we develop an adaptive controller for time-invariant, state-dependent adversarial sensor and actuator attacks in the face of stochastic exogenous disturbances. We show that the proposed controller guarantees uniform ultimate boundedness of the closed-loop dynamical system in a mean-square sense. We further discuss the practicality of the proposed approach and provide a numerical example involving the lateral directional dynamics of an aircraft to illustrate the efficacy of the proposed adaptive control architecture.