Towards Distributed Accommodation of Covert Attacks in Interconnected Systems

The problem of mitigating maliciously injected signals in interconnected systems is dealt with in this paper. We consider the class of covert attacks, as they are stealthy and cannot be detected by conventional means in centralized settings. Distributed architectures can be leveraged for revealing such stealthy attacks by exploiting communication and local model knowledge. We show how such detection schemes can be improved to estimate the action of an attacker and we propose an accommodation scheme in order to mitigate or neutralize abnormal behavior of a system under attack

Distributed Adaptive Fault-Tolerant Control of Uncertain Multi-Agent Systems

This paper presents an adaptive fault-tolerant control (FTC) scheme for a class of nonlinear uncertain multi-agent systems. A local FTC scheme is designed for each agent using local measurements and suitable information exchanged between neighboring agents. Each local FTC scheme consists of a fault diagnosis module and a reconfigurable controller module comprised of a baseline controller and two adaptive fault-tolerant controllers activated after fault detection and after fault isolation, respectively. Under certain assumptions, the closed-loop system's stability and leader-follower consensus properties are rigorously established under different modes of the FTC system, including the time-period before possible fault detection, between fault detection and possible isolation, and after fault isolation

Identifying Security-Critical Cyber-Physical Components in Industrial Control Systems

In recent years, Industrial Control Systems (ICS) have become an appealing target for cyber attacks, having massive destructive consequences. Security metrics are therefore essential to assess their security posture. In this paper, we present a novel ICS security metric based on AND/OR graphs that represent cyber-physical dependencies among network components. Our metric is able to efficiently identify sets of critical cyber-physical components, with minimal cost for an attacker, such that if compromised, the system would enter into a non-operational state. We address this problem by efficiently transforming the input AND/OR graph-based model into a weighted logical formula that is then used to build and solve a Weighted Partial MAX-SAT problem. Our tool, META4ICS, leverages state-of-the-art techniques from the field of logical satisfiability optimisation in order to achieve efficient computation times. Our experimental results indicate that the proposed security metric can efficiently scale to networks with thousands of nodes and be computed in seconds. In addition, we present a case study where we have used our system to analyse the security posture of a realistic water transport network. We discuss our findings on the plant as well as further security applications of our metric.Comment: Keywords: Security metrics, industrial control systems, cyber-physical systems, AND-OR graphs, MAX-SAT resolutio

A Robust Nonlinear Observer-based Approach for Distributed Fault Detection of Input-Output Interconnected Systems

This paper develops a nonlinear observer-based approach for distributed fault detection of a class of interconnected input–output nonlinear systems, which is robust to modeling uncertainty and measurement noise. First, a nonlinear observer design is used to generate the residual signals required for fault detection. Then, a distributed fault detection scheme and the corresponding adaptive thresholds are designed based on the observer characteristics and, at the same time, filtering is used in order to attenuate the effect of measurement noise, which facilitates less conservative thresholds and enhanced robustness. Finally, a fault detectability condition characterizing quantitatively the class of detectable faults is derived

Control of a Mixed Autonomy Signalised Urban Intersection: An Action-Delayed Reinforcement Learning Approach

We consider a mixed autonomy scenario where the traffic intersection controller decides whether the traffic light will be green or red at each lane for multiple traffic-light blocks. The objective of the traffic intersection controller is to minimize the queue length at each lane and maximize the outflow of vehicles over each block. We consider that the traffic intersection controller informs the autonomous vehicle (AV) whether the traffic light will be green or red for the future traffic-light block. Thus, the AV can adapt its dynamics by solving an optimal control problem. We model the decision process of the traffic intersection controller as a deterministic delay Markov decision process owing to the delayed action by the traffic controller. We propose Reinforcement-learning based algorithm to obtain the optimal policy. We show - empirically - that our algorithm converges and reduces the energy costs of AVs drastically as the traffic controller communicates with the AVs.Comment: Accepted for Publication at 24th IEEE International Conference on Intelligent Transportation (ITSC'2021

A Deadbeat Observer for Two and Three-dimensional LTI Systems by a Time/Output-Dependent State Mapping

The problem of deadbeat state reconstruction for non-autonomous linear systems has been solved since several decades, but all the architectures formulated since now require either high-gain output injection, which amplifies measurement noises (e.g., in the case of sliding-mode observers), either state augmentation, which yields a non-minimal realization of the deadbeat observer (e.g., in the case of integral methods and delay-based methods). In this context, the present paper presents, for the first time, a finite-time observer for continuous-time linear systems enjoying minimal linear-time-varying dynamics, that is, the observer has the same order of the observed system. The key idea behind the proposed method is the introduction of an almost-always invertible time/output-dependent state mapping which allows to recast the dynamics of the system in a new observer canonical form whose initial conditions are known

Event-Triggered Action-Delayed Reinforcement Learning Control of a Mixed Autonomy Signalised Urban Intersection

We propose an event-triggered framework for deciding the traffic light at each lane in a mixed autonomy scenario. We deploy the decision after a suitable delay, and events are triggered based on the satisfaction of a predefined set of conditions. We design the trigger conditions and the delay to increase the vehicles’ throughput. This way, we achieve full exploitation of autonomous vehicles (AVs) potential. The ultimate goal is to obtain vehicle-flows led by AVs at the head. We formulate the decision process of the traffic intersection controller as a deterministic delayed Markov decision process, i.e., the action implementation and evaluation are delayed. We propose a Reinforcement Learning based model-free algorithm to obtain the optimal policy. We show - by simulations - that our algorithm converges, and significantly reduces the average wait-time and the queues length as the fraction of the AVs increases. Our algorithm outperforms our previous work [1] by a quite significant amount