The Experts below are selected from a list of 16977 Experts worldwide ranked by ideXlab platform
Henry Leung - One of the best experts on this subject based on the ideXlab platform.
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A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids
IEEE Access, 2019Co-Authors: Hadis Karimipour, Ali Dehghantanha, Reza M. Parizi, Kim-kwang Raymond Choo, Henry LeungAbstract:Smart grid technology increases reliability, security, and efficiency of the electrical grids. However, its strong dependencies on digital communication technology bring up new vulnerabilities that need to be considered for efficient and reliable power distribution. In this paper, an unsupervised anomaly detection based on statistical correlation between measurements is proposed. The goal is to design a scalable anomaly detection engine suitable for large-scale smart grids, which can differentiate an actual fault from a disturbance and an intelligent Cyber-Attack. The proposed method applies feature extraction utilizing symbolic dynamic filtering (SDF) to reduce computational burden while discovering causal interactions between the subsystems. The simulation results on IEEE 39, 118, and 2848 bus systems verify the performance of the proposed method under different operation conditions. The results show an accuracy of 99%, true positive rate of 98%, and false positive rate of less than 2%
Xiaodong Wang - One of the best experts on this subject based on the ideXlab platform.
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Sequential Cyber-Attack detection in the large-scale smart grid system
2015 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2015Co-Authors: Yasin Yilmaz, Xiaodong WangAbstract:This paper investigates the sequential detection of Cyber-Attack in smart grid system, which undermines the power system state estimation by injecting malicious data to the monitoring meters. To overcome the challenges raised by unpredictable Attack features such as injected data information and the set of affected meters, we propose a generalized Cumulative Sum (CUSUM) detector based on the generalized likelihood ratio. Furthermore, to alleviate the exponentially growing computational burden, we further refine the detector such that its computational complexity scales linearly with the number of meters, which is usually large in the smart grid system.
Haralambos Mouratidis - One of the best experts on this subject based on the ideXlab platform.
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Cyber Attack path discovery in a dynamic supply chain maritime risk management system
Computer Standards & Interfaces, 2018Co-Authors: Nikolaos Polatidis, Michalis Pavlidis, Haralambos MouratidisAbstract:Maritime port infrastructures rely on the use of information systems for collaboration, while a vital part of collaborating is to provide protection to these systems. Attack graph analysis and risk assessment provide information that can be used to protect the assets of a network from Cyber-Attacks. Furthermore, Attack graphs provide functionality that can be used to identify vulnerabilities in a network and how these can be exploited by potential Attackers. Existing Attack graph generation methods are inadequate in satisfying certain requirements necessary in a dynamic supply chain risk management environment, since they do not consider variables that assist in exploring specific network parts that satisfy certain criteria, such as the entry and target points, the propagation length and the location and capability of the potential Attacker. In this paper, we present a Cyber-Attack path discovery method that is used as a component of a maritime risk management system. The method uses constraints and Depth-first search to effectively generate Attack graphs that the administrator is interested in. To support our method and to show its effectiveness we have evaluated it using real data from a maritime supply chain.
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Cyber Attack path discovery in a dynamic supply chain maritime risk management system
Computer Standards & Interfaces, 2018Co-Authors: Nikolaos Polatidis, Michalis Pavlidis, Haralambos MouratidisAbstract:Maritime port infrastructures rely on the use of information systems for collaboration, while a vital part of collaborating is to provide protection to these systems. Attack graph analysis and risk assessment provide information that can be used to protect the assets of a network from Cyber-Attacks. Furthermore, Attack graphs provide functionality that can be used to identify vulnerabilities in a network and how these can be exploited by potential Attackers. Existing Attack graph generation methods are inadequate in satisfying certain requirements necessary in a dynamic supply chain risk management environment, since they do not consider variables that assist in exploring specific network parts that satisfy certain criteria, such as the entry and target points, the propagation length and the location and capability of the potential Attacker. In this paper, we present a Cyber-Attack path discovery method that is used as a component of a maritime risk management system. The method uses constraints and Depth-first search to effectively generate Attack graphs that the administrator is interested in. To support our method and to show its effectiveness we have evaluated it using real data from a maritime supply chain.
Haibo He - One of the best experts on this subject based on the ideXlab platform.
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Cyber Attack recovery strategy for smart grid based on deep reinforcement learning
IEEE Transactions on Smart Grid, 2019Co-Authors: Haibo HeAbstract:The integration of Cyber-physical system increases the vulnerabilities of critical power infrastructures. Once the malicious Attackers take the substation control authorities, they can trip all the transmission lines to block the power transfer. As a consequence, asynchrony will emerge between the separated regions which had been interconnected by these transmission lines. In order to recover from the Attack, a straightforward way is to reclose these transmission lines once we detect the Attack. However, this may cause severe impacts on the power system, such as current inrush and power swing. Therefore, it is critical to properly choose the reclosing time to mitigate these impacts. In this paper, we propose a recovery strategy to reclose the tripped transmission lines at the optimal reclosing time. In particular, a deep reinforcement learning (RL) framework is adopted to endow the strategy with the adaptability of uncertain Cyber-Attack scenarios and the ability of real-time decision-making. In this framework, an environment is established to simulate the power system dynamics during the Attack-recovery process and generate the training data. With these data, the deep RL based strategy can be trained to determine the optimal reclosing time. Numerical results show that the proposed strategy can minimize the Cyber-Attack impacts under different scenarios.
Hadis Karimipour - One of the best experts on this subject based on the ideXlab platform.
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A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids
IEEE Access, 2019Co-Authors: Hadis Karimipour, Ali Dehghantanha, Reza M. Parizi, Kim-kwang Raymond Choo, Henry LeungAbstract:Smart grid technology increases reliability, security, and efficiency of the electrical grids. However, its strong dependencies on digital communication technology bring up new vulnerabilities that need to be considered for efficient and reliable power distribution. In this paper, an unsupervised anomaly detection based on statistical correlation between measurements is proposed. The goal is to design a scalable anomaly detection engine suitable for large-scale smart grids, which can differentiate an actual fault from a disturbance and an intelligent Cyber-Attack. The proposed method applies feature extraction utilizing symbolic dynamic filtering (SDF) to reduce computational burden while discovering causal interactions between the subsystems. The simulation results on IEEE 39, 118, and 2848 bus systems verify the performance of the proposed method under different operation conditions. The results show an accuracy of 99%, true positive rate of 98%, and false positive rate of less than 2%
