Secure State Estimation Against Sensor Attacks in the Presence of Noise

Mishra, Shaunak; Shoukry, Yasser; Karamchandani, Nikhil; Diggavi, Suhas; Tabuada, Paulo

doi:10.1109/tcns.2016.2606880

Cited by 126 publications

(112 citation statements)

References 15 publications

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“…where x k is the system state at time k, u k denotes the controller commands to actuators, y k are the sensor measurements, k is perturbation noise, e k is sensor noise, and A, B, C, D are matrices modeling the dynamics of the system. This approach has been used in previous studies, such as [30]- [32]. The limitations of linear dynamical system modeling include the requirement for controller command measurement, a requirement which is not met in most datasets.…”

Section: Related Workmentioning

confidence: 99%

Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

Kravchik

Shabtai

2018

Proceedings of the 2018 Workshop on Cyber-Physical Systems Security and PrivaCy

251

167

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Industrial control systems (ICSs) are widely used and vital to industry and society. Their failure can have severe impact on both economics and human life. Hence, these systems have become an attractive target for attacks, both physical and cyber. A number of attack detection methods have been proposed, however they are characterized by a low detection rate, a substantial false positive rate, or are system specific. In this paper, we study an attack detection method based on simple and lightweight neural networks, namely, 1D convolutions and autoencoders. We apply these networks to both the time and frequency domains of the collected data and discuss pros and cons of each approach. We evaluate the suggested method on three popular public datasets and achieve detection rates matching or exceeding previously published detection results, while featuring small footprint, short training and detection times, and generality. We also demonstrate the effectiveness of PCA, which, given proper data preprocessing and feature selection, can provide high attack detection scores in many settings. Finally, we study the proposed method's robustness against adversarial attacks, that exploit inherent blind spots of neural networks to evade detection while achieving their intended physical effect. Our results show that the proposed method is robust to such evasion attacks: in order to evade detection, the attacker is forced to sacrifice the desired physical impact on the system. This finding suggests that neural networks trained under the constraints of the laws of physics can be trusted more than networks trained under more flexible conditions.

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Section: Related Workmentioning

confidence: 99%

Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

Kravchik

Shabtai

2018

Proceedings of the 2018 Workshop on Cyber-Physical Systems Security and PrivaCy

251

167

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“…The mitigation signal δ[t] can be generated using existing mitigation approaches [35], [36], [37], [38]. Specifically, once the attack is detected, a conservative mitigation approach is to ignore the sensor measurements completely, and drive the system based only on the model.…”

Section: A System Modelmentioning

confidence: 99%

Performance and Resilience of Cyber-Physical Control Systems With Reactive Attack Mitigation

Lakshminarayana

Karachiwala

Teng³

et al. 2019

IEEE Trans. Smart Grid

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This paper studies the performance and resilience of a linear cyber-physical control system (CPCS) with attack detection and reactive attack mitigation in the context of power grids. It addresses the problem of deriving an optimal sequence of false data injection attacks that maximizes the state estimation error of the power system. The results provide basic understanding about the limit of the attack impact. The design of the optimal attack is based on a Markov decision process (MDP) formulation, which is solved efficiently using the value iteration method. We apply the proposed framework to the voltage control system of power grids and run extensive simulations using PowerWorld. The results show that our framework can accurately characterize the maximum state estimation errors caused by an attacker who carefully designs the attack sequence to strike a balance between the attack magnitude and stealthiness, due to the simultaneous presence of attack detection and mitigation. Moreover, based on the proposed framework, we analyze the impact of false positives and negatives in detecting attacks on the system performance. The results are important for the system defenders in the joint design of attack detection and mitigation to reduce the impact of these attack detection errors.Finally, as MDP solutions are not scalable for high-dimensional systems, we apply Q-learning with linear and non-linear (neural networks based) function approximators to solve the attacker's problem in these systems and compare their performances.

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“…Recently, dynamic state estimation with some Byzantine sensors has been discussed. Most approaches in the existing literature can be classified into two categories: stacked measurements [6]- [8] and Kalman filter decomposition [9], [10]. Fawzi et al [6] used the stacked measurements from time k to k + T − 1 to estimate the state at time k and provided l 0 and l 1 -based state estimation procedures.…”

mentioning

confidence: 99%

“…Pajic et al [7] extended the deterministic systems in [6] to ones with bounded measurement noises and obtained upper bounds of estimation error for both l 0 and l 1 -based estimators. Mishar et al [8] studied stochastic systems with unbounded noises and proposed a notion of ǫ-effective attack. The state estimation there is in essence an attack detection problem; a Chi-squared test is applied to the residues and the standard Kalman filter output based on the measurements from the largest set of sensors that are deemed ǫ-effective attack-free is used as the state estimate.…”

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confidence: 99%

Secure State Estimation With Byzantine Sensors: A Probabilistic Approach

Ren

Chen

et al. 2020

IEEE Trans. Automat. Contr.

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This paper studies static state estimation in multi-sensor settings, with a caveat that an unknown subset of the sensors are compromised by an adversary, whose measurements can be manipulated arbitrarily. The attacker is able to compromise q out of m sensors. A new performance metric, which quantifies the asymptotic decay rate for the probability of having an estimation error larger than δ, is proposed. We develop an optimal estimator for the new performance metric with a fixed δ, which is the Chebyshev center of a union of ellipsoids. We further provide an estimator that is optimal for every δ, for the special case where the sensors are homogeneous. Numerical examples are given to elaborate the results.

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Secure State Estimation Against Sensor Attacks in the Presence of Noise

Cited by 126 publications

References 15 publications

Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

Performance and Resilience of Cyber-Physical Control Systems With Reactive Attack Mitigation

Secure State Estimation With Byzantine Sensors: A Probabilistic Approach

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