Securing state reconstruction under sensor and actuator attacks: Theory and design

Showkatbakhsh, Mehrdad; Shoukry, Yasser; Diggavi, Suhas; Tabuada, Paulo

doi:10.1016/j.automatica.2020.108920

Cited by 31 publications

(13 citation statements)

References 40 publications

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“…For system (1), ψ(k), ϕ(k) and a(k) are uncertain. And in order to design a secure state observer, the following assumptions, which are frequently used in the literatures [14], [24], [25], [28], [29], [34], are essential. Assumption 1.…”

Section: Preliminaries and Problem Formulationmentioning

confidence: 99%

Observer Design for Cyber-Physical Systems With State Delay and Sparse Sensor Attacks

Zhang

Lin

et al. 2021

IEEE Access

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Section: Preliminaries and Problem Formulationmentioning

confidence: 99%

Observer Design for Cyber-Physical Systems With State Delay and Sparse Sensor Attacks

Zhang

Lin

et al. 2021

IEEE Access

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“…Till now, there are two mainstream techniques, i.e., optimization relaxation (OR) and brute force search (BFS), to cope with the secure state estimation problem of CPSs. Several algorithms such as satisfiability modulo solvers [22], projected gradientdescent paradigms [23], optimal graph searching [24] and L 1 /L r decoders [25] belong to OR and the basic idea of which is to find the optimal solution in polynomial time. For BFS, a number of excellent results including observability Gramians [26], switched counteraction principle (SCP) [19] and identification filter [18] have been obtained.…”

Section: Introductionmentioning

confidence: 99%

Sparse Actuator and Sensor Attacks Reconstruction for Linear Cyber-Physical Systems With Sliding Mode Observer

Yang

Yin

Han

et al. 2022

IEEE Trans. Ind. Inf.

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Driven by the rapid development of modern industrial processes, Cyber-Physical Systems (CPSs), which tightly conjoin computational and physical resources, have become ever more prevalent during recent years. However, due to the intrinsical vulnerability of the cyber layer, the system performances of CPSs are easily degraded by malicious false data injection (FDI) attacks which are launched by adversary. In this work, the issue of secure reconstruction is considered for linear CPSs with simultaneous sparse actuator and sensor attacks. First, an adaptive counteraction searching strategy is proposed to identify the potential combinational attack mode. In this way, malicious FDI attacks are excluded. Second, by constructing a descriptor switched sliding mode observer (SMO), the sparse FDI attacks and the system state are reconstructed effectively. Meanwhile, sufficient conditions of the error convergence can be derived. Finally, a numerical simulation is utilized to illustrate the applicability of the proposed theoretical derivation.

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“…For example, the target may be subject to arbitrary unknown disturbances which are difficult, if possible, to statistically interpret or model. Typical examples in applications include systems under fault/attacks [20]- [22], tracking/localisation of targets subject to abrupt maneuvers [23]- [27], advanced vehicle applications under complex tireground interactions [28]- [31], estimation of unmeasured forces in grasping/manipulation [32]- [33], etc. In fact, filtering and estimation under arbitrary unknown inputs have received much attention in the control literature [34]- [37], and have found numerous applications, including robustness/security analysis and synthesis of resilient autonomous robots and connected vehicle systems [38]- [41].…”

Section: Introductionmentioning

confidence: 99%

Active Information Acquisition under Arbitrary Unknown Disturbances

Jennifer¹,

Kong²,

Sukkarieh³

2021

Preprint

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Trajectory optimization of sensing robots to actively gather information of targets has received much attention in the past. It is well-known that under the assumption of linear Gaussian target dynamics and sensor models the stochastic Active Information Acquisition problem is equivalent to a deterministic optimal control problem. However, the abovementioned assumptions regarding the target dynamic model are limiting. In real-world scenarios, the target may be subject to disturbances whose models or statistical properties are hard or impossible to obtain. Typical scenarios include abrupt maneuvers, jumping disturbances due to interactions with the environment, anomalous misbehaviors due to system faults/attacks, etc. Motivated by the above considerations, in this paper we consider targets whose dynamic models are subject to arbitrary unknown inputs whose models or statistical properties are not assumed to be available. In particular, with the aid of an unknown input decoupled filter, we formulate the sensor trajectory planning problem to track evolution of the target state and analyse the resulting performance for both the state and unknown input evolution tracking. Inspired by concepts of Reduced Value Iteration, a suboptimal solution that expands a search tree via Forward Value Iteration with informativeness-based pruning is proposed. Concrete suboptimality performance guarantees for tracking both the state and the unknown input are established. Numerical simulations of a target tracking example are presented to compare the proposed solution with a greedy policy.

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Securing state reconstruction under sensor and actuator attacks: Theory and design

Cited by 31 publications

References 40 publications

Observer Design for Cyber-Physical Systems With State Delay and Sparse Sensor Attacks

Observer Design for Cyber-Physical Systems With State Delay and Sparse Sensor Attacks

Sparse Actuator and Sensor Attacks Reconstruction for Linear Cyber-Physical Systems With Sliding Mode Observer

Active Information Acquisition under Arbitrary Unknown Disturbances

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