Multi-Agent Approach for Enhancing Security of Protection Schemes in Cyber-Physical Energy Systems

Rahman, M. S.; Mahmud, M. A.; Oo, Amanullah Maung Than; Pota, H. R.

doi:10.1109/tii.2016.2612645

Cited by 119 publications

(69 citation statements)

References 21 publications

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“…In [33], the authors used behavior rules to specify the safe system state for medical Cyber Physical Systems (CPSs) and monitor deviation from these rules. Distributed invariant-based mechanisms for smart grids are presented in [34] and [35]. In [35], detection is based on observing the physical state of the shared system, detecting the power conservation invariants' violation and identifying the rogue component by the invariants' verification in its topological neighborhood.…”

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

222

164

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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

222

164

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“…One kind is the simulation method, such as Monte Carlo simulation [10][11][12][13][14], which is mainly utilized to get reliability indices through statistical analysis and stochastic sampling of the assumed probabilistic distribution, where this kind of method is intuitive [15]. The other kind is the analytic methods, such as Markov model analysis [16][17][18][19][20][21][22][23][24][25], neural networks [26][27][28][29][30][31][32], fault trees [33][34][35][36][37], etc., which are typically applied to develop a probabilistic model with the information of the construction, the function, or the logic relationship of the considered object and calculate the reliability indices by iteration. For these methods, the physical concept is clear and the accuracy is guaranteed, where, in some cases, the calculation burden increases rapidly with the increasing of the system scale.…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment of Smart Substation Relay Protection System Based on Markov Model and Risk Transfer Network

et al. 2020

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To effectively guarantee a secure and stable operation of a smart substation, it is essential to develop a relay protection system considering the real-time online operation state evaluation and the risk assessment of that substation. In this paper, based on action data, defect data, and network message information of the system protection device (PD), a Markov model-based operation state evaluation method is firstly proposed for each device in the relay protection system (RPS). Then, the risk assessment of RPS in the smart substation is carried out by utilizing the risk transfer network. Finally, to highly verify the usefulness and the effectiveness of the proposed method, a case study of a typical 220 kV substation is provided. It follows from the case study that the developed method can achieve a better improvement for the maintenance plan of the smart substation.

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“…In order to guarantee the security of a CPS, detecting the attacks in time and taking some protection measures properly are musts to maintain the integrity and reliability of the system. A distributed multiagent scheme of detecting and identifying cyber threats for a power grid system is presented . Palleti et al explored the subject of the online detections of cyber‐attacks by using conventional Kalman filter and cumulative sum test fault detection and isolation (FDI) methods.…”

Section: Introductionmentioning

confidence: 99%

“…A distributed multiagent scheme of detecting and identifying cyber threats for a power grid system is presented. 15 Palleti et al 16 explored the subject of the online detections of cyber-attacks by using conventional Kalman filter and cumulative sum test fault detection and isolation (FDI) methods. Huang et al 17 concerned with reliable control problems of cyber-physical systems against a class of frequency constrained sensor and actuator attacks.…”

Section: Introductionmentioning

confidence: 99%

Sensor attack detection and isolation based on sliding mode observer for cyber‐physical systems

Zhu

Zhang

2020

Adaptive Control & Signal

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Summary This article investigates the sensor attack detection and isolation problems for cyber‐physical systems under attacked situation. To begin with, an overall system is constructed for the network of dynamic system and all the issues are dealt with for the overall system instead of every subsystem. Then, we prove that the preconditions which are made on the original subsystem can be transferred on to the overall system. After this, for the overall system, a sliding mode observer that is robust to the disturbance but sensitive to the sensor attack is designed as sensor attack detector to issue an alarm when at least one subsystem is attacked. In addition, we cope with the sensor fault isolation problem by building an augmented descriptor system for the overall system and we further prove that the precondition can be kept for the augmented descriptor system. Then, another robust sliding mode observer is designed to reach the goal of attack isolation. Finally, a simulation example is given to verify the effectiveness of the proposed methods.

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Multi-Agent Approach for Enhancing Security of Protection Schemes in Cyber-Physical Energy Systems

Cited by 119 publications

References 21 publications

Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

Risk Assessment of Smart Substation Relay Protection System Based on Markov Model and Risk Transfer Network

Sensor attack detection and isolation based on sliding mode observer for cyber‐physical systems

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