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Peng-Shuai Cui
College of Computer, National University of Defense Technology, Changsha 410073, China

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Journal article
Published: 13 September 2018 in Electronics
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In this paper, a failure model of Cyber-Physical systems and an attack model are proposed. We divide the attacks into three kinds: simultaneous attack, sequential attack and composite attack. Through numerical simulations, we find that: (1) the sequential attack may bring more damage in single physical systems; (2) the coupling process of cyber system and physical systems makes it possible that sequential attack causes more damage than simultaneous attacks when the attackers only attack the cyber system; (3) with some target sets, composite attack leads to more failures than both simultaneous attack and sequential attack. The above results suggest that defenders should take all the three kinds of attacks into account when they select the critical nodes.

ACS Style

Pengshuai Cui; Peidong Zhu; Peng Xun; Chengcheng Shao. Robustness of Cyber-Physical Systems against Simultaneous, Sequential and Composite Attack. Electronics 2018, 7, 196 .

AMA Style

Pengshuai Cui, Peidong Zhu, Peng Xun, Chengcheng Shao. Robustness of Cyber-Physical Systems against Simultaneous, Sequential and Composite Attack. Electronics. 2018; 7 (9):196.

Chicago/Turabian Style

Pengshuai Cui; Peidong Zhu; Peng Xun; Chengcheng Shao. 2018. "Robustness of Cyber-Physical Systems against Simultaneous, Sequential and Composite Attack." Electronics 7, no. 9: 196.

Journal article
Published: 04 June 2018 in Electronics
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False data injection (FDI) attack is a hot topic in large-scale Cyber-Physical Systems (CPSs), which can cause bad state estimation of controllers. In this paper, we focus on FDI detection on transmission lines of the smart grid. We propose a novel and effective detection framework to identify FDI attacks. Different from the previous methods, there are multi-tier detectors which utilize edge nodes such as the programmable logic controllers (PLCs) instead of the central controller to detect attacks. The proposed framework can decrease the transmission time of data to reduce the latency of decisions because many sensory data need not be transmitted to the central controller for detection. We also develop a detection algorithm which utilizes classifiers based on machine learning to identify FDI. The training process is split from every edge node and is placed on the central node. The detectors are lightweight and are properly adopted in our detection framework. Our simulation experiments show that the proposed detection framework can provide better detection results than the existing detection approaches.

ACS Style

Peng Xun; Peidong Zhu; Zhenyu Zhang; Pengshuai Cui; Yinqiao Xiong. Detectors on Edge Nodes against False Data Injection on Transmission Lines of Smart Grid. Electronics 2018, 7, 89 .

AMA Style

Peng Xun, Peidong Zhu, Zhenyu Zhang, Pengshuai Cui, Yinqiao Xiong. Detectors on Edge Nodes against False Data Injection on Transmission Lines of Smart Grid. Electronics. 2018; 7 (6):89.

Chicago/Turabian Style

Peng Xun; Peidong Zhu; Zhenyu Zhang; Pengshuai Cui; Yinqiao Xiong. 2018. "Detectors on Edge Nodes against False Data Injection on Transmission Lines of Smart Grid." Electronics 7, no. 6: 89.

Journal article
Published: 17 February 2018 in Journal of Forensic and Legal Medicine
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The near-repeat effect is a well-known phenomenon in crime analysis. The classic research methods focus on two aspects. One is the geographical factor, which indicates the influence of a certain crime risk on other similar crime incidents in nearby places. The other is the social network, which demonstrates the contacts of the offenders and explain ”near” as degrees instead of geographic distances. In our work, these coarse-grained patterns discovering methods are summarized as bundled-clues techniques. In this paper, we propose a knotted-clues method. Adopting a data science perspective, we make use of a data interpretative technology and discover that the near-repeat effect is not always so near in geographic or network structure. With this approach, we analyze the near-repeat patterns in all districts of the dataset, as well as in different crime types. Using open source data from Crimes in Chicago provided by Chicago Police Department, we find interesting relationships and patterns with our mining method, which have a positive effect on police deployment and decision making.

ACS Style

Ke Wang; Zhiping Cai; Peidong Zhu; Pengshuai Cui; Haoyang Zhu; Yangyang Li. Adopting data interpretation on mining fine-grained near-repeat patterns in crimes. Journal of Forensic and Legal Medicine 2018, 55, 76 -86.

AMA Style

Ke Wang, Zhiping Cai, Peidong Zhu, Pengshuai Cui, Haoyang Zhu, Yangyang Li. Adopting data interpretation on mining fine-grained near-repeat patterns in crimes. Journal of Forensic and Legal Medicine. 2018; 55 ():76-86.

Chicago/Turabian Style

Ke Wang; Zhiping Cai; Peidong Zhu; Pengshuai Cui; Haoyang Zhu; Yangyang Li. 2018. "Adopting data interpretation on mining fine-grained near-repeat patterns in crimes." Journal of Forensic and Legal Medicine 55, no. : 76-86.

Journal article
Published: 21 October 2017 in Sensors
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A cyber-physical attack in the industrial Internet of Things can cause severe damage to physical system. In this paper, we focus on the command disaggregation attack, wherein attackers modify disaggregated commands by intruding command aggregators like programmable logic controllers, and then maliciously manipulate the physical process. It is necessary to investigate these attacks, analyze their impact on the physical process, and seek effective detection mechanisms. We depict two different types of command disaggregation attack modes: (1) the command sequence is disordered and (2) disaggregated sub-commands are allocated to wrong actuators. We describe three attack models to implement these modes with going undetected by existing detection methods. A novel and effective framework is provided to detect command disaggregation attacks. The framework utilizes the correlations among two-tier command sequences, including commands from the output of central controller and sub-commands from the input of actuators, to detect attacks before disruptions occur. We have designed components of the framework and explain how to mine and use these correlations to detect attacks. We present two case studies to validate different levels of impact from various attack models and the effectiveness of the detection framework. Finally, we discuss how to enhance the detection framework.

ACS Style

Peng Xun; Pei-Dong Zhu; Yi-Fan Hu; Peng-Shuai Cui; Yan Zhang. Command Disaggregation Attack and Mitigation in Industrial Internet of Things. Sensors 2017, 17, 2408 .

AMA Style

Peng Xun, Pei-Dong Zhu, Yi-Fan Hu, Peng-Shuai Cui, Yan Zhang. Command Disaggregation Attack and Mitigation in Industrial Internet of Things. Sensors. 2017; 17 (10):2408.

Chicago/Turabian Style

Peng Xun; Pei-Dong Zhu; Yi-Fan Hu; Peng-Shuai Cui; Yan Zhang. 2017. "Command Disaggregation Attack and Mitigation in Industrial Internet of Things." Sensors 17, no. 10: 2408.

Journal article
Published: 01 March 2017 in Physica A: Statistical Mechanics and its Applications
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We propose a capability based dependency model of interdependent network that takes two node dependency properties into account. One is support capability and the other is required capability. The redundancy degree of an interdependent network is also defined, whose value is the ratio of its total support capability and total required capability. Through the numerical simulations, we found that: (1) Interdependent networks without redundant support-dependence links are extremely vulnerable, even the failure of one node could cause the collapse of whole network; (2) Increasing support-dependence links and redistributing the nodes’ dependency properties can enhance the robustness of network without changing its redundancy degree; (3) Improving the redundancy degree could enhance network robustness without adding support-dependence links. These conclusions enlighten the design of interdependent networks: when network’s redundancy degree is fixed, we can take strategy from results (2),and when network structure is settled, we can apply strategy from results (3).

ACS Style

Pengshuai Cui; Peidong Zhu; Chengcheng Shao; Peng Xun. Cascading failures in interdependent networks due to insufficient received support capability. Physica A: Statistical Mechanics and its Applications 2017, 469, 777 -788.

AMA Style

Pengshuai Cui, Peidong Zhu, Chengcheng Shao, Peng Xun. Cascading failures in interdependent networks due to insufficient received support capability. Physica A: Statistical Mechanics and its Applications. 2017; 469 ():777-788.

Chicago/Turabian Style

Pengshuai Cui; Peidong Zhu; Chengcheng Shao; Peng Xun. 2017. "Cascading failures in interdependent networks due to insufficient received support capability." Physica A: Statistical Mechanics and its Applications 469, no. : 777-788.