Attack-Resilient State Estimation for Noisy Dynamical Systems

Pajić, Miroslav; Lee, Insup; Pappas, George J.

doi:10.1109/tcns.2016.2607420

Cited by 177 publications

(128 citation statements)

References 22 publications

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“…In the following, we consider the system with

card false(scriptK false) \leq s_{\max}

, where

s_{\max}

is the maximal number of s for which the system is observable under an s ‐sparse attack in the absence of the unknown input. It is easy to see that

card false(scriptK false) \leq s_{\max}

is a necessary condition for recovering/reconstructing the system state, and this condition is also required to be satisfied in other works . Note that the parameter

s_{\max}

can be determined beforehand based on Lemma , and then,

s_{\max}

is assumed to be known.…”

Section: Observer Design For System With Unknown Input and Adversariamentioning

confidence: 99%

“…Thus, the robustness of the aforementioned decoders/estimators with respect to the unknown inputs becomes a critical issue. Motivated by the pioneering works of Fawzi et al, l 0 ‐ and l 1 ‐based estimators were proposed in the works of Pajic et al for the CPSs with sensor attacks and noises. In the works of Han et al, a general class of convex optimization‐based estimators, as well as the attack‐resilience analysis, was given for the case of static error correction in the presence of noises.…”

Section: Introductionmentioning

confidence: 99%

“…One should note that a common characteristic of the existing results is that the state estimation error possesses L ∞ – L ∞ (ie, peak‐to‐peak) performance with respect to the noises or disturbances, that is, the bound of the state estimation error is proportional to the magnitude of the noises or disturbances. However, the performance cannot be optimized within the existing framework, which may potentially result in a poor estimation performance.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, the mismatched part will be suppressed in terms of L 2 ‐gain rejection property. Different from the results where L ∞ – L ∞ performance is adopted and cannot be optimized, the proposed L 2 ‐gain rejection performance index can be minimized as much as possible with the help of the LMI technique. For most of the existing linear and nonlinear unknown input observers, a necessary condition for the construction of such observers is that the observer matching condition is satisfied . However, this condition is hard to satisfy for many practical systems .…”

Section: Introductionmentioning

confidence: 99%

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Secure estimation for cyber‐physical systems with adversarial attacks and unknown inputs: An L₂‐gain method

Xie

Yang

2017

Intl J Robust & Nonlinear

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Summary This paper investigates the attack‐resilient state estimation problem for linear systems with adversarial attacks and unknown inputs, where the upper bound of the unknown inputs is unknown. It is assumed that the attacker has limited resources and can only manipulate a certain number of sensors. In most of the existing observer design approaches for the systems with unknown inputs even in the absence of attacks, the observer matching condition should be satisfied. To overcome this restriction, a novel switched observer is proposed, where the matched unknown inputs will be completely compensated by means of the outputs and the mismatched part will be suppressed in terms of L2‐gain rejection property. Meanwhile, the observer can provide an attack‐resilient state estimation. Compared with the existing results, the proposed observer can guarantee that the resulting observer error system is stable with unknown input attenuation level γ that can be optimized. Finally, a simulation example of an unmanned ground vehicle is provided to show the effectiveness of the proposed approach.

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“…In the following, we consider the system with

card false(scriptK false) \leq s_{\max}

, where

s_{\max}

is the maximal number of s for which the system is observable under an s ‐sparse attack in the absence of the unknown input. It is easy to see that

card false(scriptK false) \leq s_{\max}

is a necessary condition for recovering/reconstructing the system state, and this condition is also required to be satisfied in other works . Note that the parameter

s_{\max}

can be determined beforehand based on Lemma , and then,

s_{\max}

is assumed to be known.…”

Section: Observer Design For System With Unknown Input and Adversariamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Secure estimation for cyber‐physical systems with adversarial attacks and unknown inputs: An L₂‐gain method

Xie

Yang

2017

Intl J Robust & Nonlinear

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show abstract

“…The problem of estimating the internal state of a system, when sensors and/or actuators can be corrupted by a malicious attacker, is called "secure state estimation" problem [7]. The secure state estimation problem in presence of sparse attacks has been investigated for two classes of systems: linear systems (both in the noiseless case [7], [8] and in the noisy scenario [20], [30], [19]), and for nonlinear differentially flat systems [28]. Instead, we provide the first contribution to the study of the secure state estimation problem for hybrid systems, due to their paramount importance when dealing with CPSs.…”

Section: Introductionmentioning

confidence: 99%

Secure state estimation for Cyber-Physical Systems

Fiore¹

Kalpa Publications in Computing

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In Cyber-Physical Systems (CPSs), physical processes, computational resources and communication capabilities are tightly interconnected. Traditionally, the physical components of a CPS are described by means of differential or difference equations, while the cyber components are modeled by means of discrete dynamics. Therefore, hybrid systems, that are heterogeneous dynamical systems characterized by the interaction of continuous and discrete dynamics, are a powerful modeling framework to deal with CPSs. Motivated by the great importance of security issues for CPSs, we characterize the observability and diagnosability properties for hybrid systems in the general case where the available information may be corrupted by an external attacker. Then, as CPSs are found in a wide range of applications, we demonstrate how to estimate the continuous state by simulating two scenarios: the control of a Direct Current (DC) Microgrid, and the control of a network of Unmanned Aerial Vehicles (UAVs) cooperatively transporting a payload.

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Robust algorithm for attack detection based on time‐varying hidden Markov model subject to outliers

Dai

Huang

2020

Adaptive Control & Signal

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The problem of robust attack detection and prediction for networked control systems in the presence of outliers is discussed in this article. The conventional hidden Markov model (HMM) is trained to learn the system behavior (ie, transitions between different operating modes) in the nominal process. The HMM with time-varying transition probabilities is used to track the attack behavior in which the adversary triggers more hazard modes to hasten fatigue of control devices by injecting attack signals with random magnitude and frequency. For different operating modes, the observations are assumed to follow different multivariate Student's t distributions instead of Gaussian distributions and thus address the robust estimation problem. The expectation maximization algorithm is used to estimate parameters. Finally, simulations are conducted to verify the effectiveness of the proposed method.

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Attack-Resilient State Estimation for Noisy Dynamical Systems

Cited by 177 publications

References 22 publications

Secure estimation for cyber‐physical systems with adversarial attacks and unknown inputs: An L₂‐gain method

Secure estimation for cyber‐physical systems with adversarial attacks and unknown inputs: An L₂‐gain method

Secure state estimation for Cyber-Physical Systems

Robust algorithm for attack detection based on time‐varying hidden Markov model subject to outliers

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Attack-Resilient State Estimation for Noisy Dynamical Systems

Cited by 177 publications

References 22 publications

Secure estimation for cyber‐physical systems with adversarial attacks and unknown inputs: An L2‐gain method

Secure estimation for cyber‐physical systems with adversarial attacks and unknown inputs: An L2‐gain method

Secure state estimation for Cyber-Physical Systems

Robust algorithm for attack detection based on time‐varying hidden Markov model subject to outliers

Contact Info

Product

Resources

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Secure estimation for cyber‐physical systems with adversarial attacks and unknown inputs: An L₂‐gain method

Secure estimation for cyber‐physical systems with adversarial attacks and unknown inputs: An L₂‐gain method