2,314 research outputs found

    Tuning Windowed Chi-Squared Detectors for Sensor Attacks

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    A model-based windowed chi-squared procedure is proposed for identifying falsified sensor measurements. We employ the widely-used static chi-squared and the dynamic cumulative sum (CUSUM) fault/attack detection procedures as benchmarks to compare the performance of the windowed chi-squared detector. In particular, we characterize the state degradation that a class of attacks can induce to the system while enforcing that the detectors do not raise alarms (zero-alarm attacks). We quantify the advantage of using dynamic detectors (windowed chi-squared and CUSUM detectors), which leverages the history of the state, over a static detector (chi-squared) which uses a single measurement at a time. Simulations using a chemical reactor are presented to illustrate the performance of our tools

    Two Compensation Strategies for Optimal Estimation in Sensor Networks with Random Matrices, Time-Correlated Noises, Deception Attacks and Packet Losses

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    Due to its great importance in several applied and theoretical fields, the signal estimation problem in multisensor systems has grown into a significant research area. Networked systems are known to suffer random flaws, which, if not appropriately addressed, can deteriorate the performance of the estimators substantially. Thus, the development of estimation algorithms accounting for these random phenomena has received a lot of research attention. In this paper, the centralized fusion linear estimation problem is discussed under the assumption that the sensor measurements are affected by random parameter matrices, perturbed by time-correlated additive noises, exposed to random deception attacks and subject to random packet dropouts during transmission. A covariance-based methodology and two compensation strategies based on measurement prediction are used to design recursive filtering and fixed-point smoothing algorithms. The measurement differencing method— typically used to deal with the measurement noise time-correlation—is unsuccessful for these kinds of systems with packet losses because some sensor measurements are randomly lost and, consequently, cannot be processed. Therefore, we adopt an alternative approach based on the direct estimation of the measurement noises and the innovation technique. The two proposed compensation scenarios are contrasted through a simulation example, in which the effect of the different uncertainties on the estimation accuracy is also evaluated.Ministerio de Ciencia e Innovacion, Agencia Estatal de InvestigacionEuropean Commission PID2021-124486NB-I0

    Design of false data injection attack on distributed process estimation

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    Herein, design of false data injection attack on a distributed cyber-physical system is considered. A stochastic process with linear dynamics and Gaussian noise is measured by multiple agent nodes, each equipped with multiple sensors. The agent nodes form a multi-hop network among themselves. Each agent node computes an estimate of the process by using its sensor observation and messages obtained from neighboring nodes, via Kalman-consensus filtering. An external attacker, capable of arbitrarily manipulating the sensor observations of some or all agent nodes, injects errors into those sensor observations. The goal of the attacker is to steer the estimates at the agent nodes as close as possible to a pre-specified value, while respecting a constraint on the attack detection probability. To this end, a constrained optimization problem is formulated to find the optimal parameter values of a certain class of linear attacks. The parameters of linear attack are learnt on-line via a combination of stochastic approximation based update of a Lagrange multiplier, and an optimization technique involving either the Karush-Kuhn-Tucker (KKT) conditions or online stochastic gradient descent. The problem turns out to be convex for some special cases. Desired convergence of the proposed algorithms are proved by exploiting the convexity and properties of stochastic approximation algorithms. Finally, numerical results demonstrate the efficacy of the attack

    Secure Estimation in V2X Networks with Injection and Packet Drop Attacks

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    Vehicle-to-anything (V2X) communications are essential for facilitating cooperative intelligent transport system (C-ITS) components such as traffic safety and traffic efficiency applications. Integral to proper functioning of C-ITS systems is sensing and telemetery. To this end, this paper examines how to ensure security in sensing systems for V2X networks. In particular, secure remote estimation of a Gauss-Markov process based on measurements done by a set of vehicles is considered. The measurements are collected by the individual vehicles and are communicated via wireless links to the central fusion center. The system is attacked by malicious or compromised vehicles with the goal of increasing the estimation error. The attack is achieved by two mechanisms: false data injection (FDI) and garbage packet injection. This paper extends a previously proposed adaptive filtering algorithm for tackling FDI to accommodate both FDI and garbage packet injection, by filtering out malicious observations and thus enabling secure estimates. The efficacy of the proposed filter is demonstrated numerically

    State of the art of cyber-physical systems security: An automatic control perspective

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    Cyber-physical systems are integrations of computation, networking, and physical processes. Due to the tight cyber-physical coupling and to the potentially disrupting consequences of failures, security here is one of the primary concerns. Our systematic mapping study sheds light on how security is actually addressed when dealing with cyber-physical systems from an automatic control perspective. The provided map of 138 selected studies is defined empirically and is based on, for instance, application fields, various system components, related algorithms and models, attacks characteristics and defense strategies. It presents a powerful comparison framework for existing and future research on this hot topic, important for both industry and academia
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