Abstract
Considering the different weights of various heterogeneity node importance in power grid and communication network and the possibility of the power grid island as well as the independent operation of the local area network, a more accurate method is proposed to evaluate the fragility of a cyber-physical system. According to the quo of grid construction and the background of cyber-physical fusion, the hierarchical system and distributed system model are established, respectively. Simulation results of conducting random attack and deliberate attack on two systems indicate that the proposed method is correct and better than others and it can identify the balance of the network structure. The distributed system is more robust than the hierarchical system under different weighting factors, while the robustness of the hierarchical system is more sensitive to the weighting factor.
Similar content being viewed by others
References
Yi T, Qian C, Mengya L et al (2016) Overview on cyber-attacks against cyber physical power system. Autom Electr Power Syst 40(17):59–69
Liang G, Weller SR, Zhao J et al (2017) The 2015 Ukraine blackout: implications for false data injection attacks. IEEE Trans Power Syst 32(4):3317–3318
Wadhawan Y, AlMajali A, Neuman C (2018) A comprehensive analysis of smart grid systems against cyber-physical attacks. Electronics 7(10):249
Shuang MA, Zhen XU, Liming WANG (2017) Set theory based modeling method of cyber physical system for power grid. Autom Electr Power Syst 41(6):1–5
Zivkovic N, Saric AT (2018) Detection of false data injection attacks using unscented Kalman filter. J Mod Power Syst Clean Energy 6(5):847–859
Shi L, Dai Q, Ni Y (2018) Cyber-physical interactions in power systems: a review of models, methods, and applications. Electr Power Syst Res 163:396–412
Yang Y, Zhao J, Liu H et al (2018) A matrix-perturbation-theory-based optimal strategy for small-signal stability analysis of large-scale power grid. Prot Control Mod Power Syst 3(1):34
Iqbal F, Siddiqui AS (2017) Optimal configuration analysis for a campus microgrid: a case study. Prot Control Mod Power Syst 2(1):23
Kontouras E, Anthony T (2018) Set-theoretic detection of data corruption attacks on cyber physical power systems. J Mod Power Syst Clean Energy 6(5):872–886
Liu B, Li Z, Chen X et al (2018) Recognition and vulnerability analysis of key nodes in power grid based on complex network centrality. IEEE Trans Circuits Syst II Express Briefs 65(3):346–350
Shengwei M, Yingying W, Laijun C (2011) Overviews and prospects of the cyber security of smart grid from the view of complex network theory. High Voltage Eng 37(3):672–679
Xingpei J, Bo W, Dichen L et al (2016) Review on interdependent networks theory and its applications in the structural vulnerability analysis of electrical cyber-physical system. Proc CSEE 36(17):4521–4532
Xingpei J, Bo W, Zhaoyang D et al (2016) Vulnerability evaluation and link addition protection strategy research of electrical cyber-physical interdependent networks. Power Syst Technol 40(6):1867–1873
Davarikia H, Barati M (2018) A tri-level programming model for attack-resilient control of power grids. J Mod Power Syst Clean Energy 6(5):918–929
Calvo JL, Tindemans SH, Strbac G (2018) Risk-based method to secure power systems against cyber-physical faults with cascading impacts: a system protection scheme application. J Mod Power Syst Clean Energy 6(5):930–943
Zhaoyang D, Fengji LUO, Liang G (2018) Blockchain: a secure, decentralized, trusted cyber infrastructure solution for future energy systems. J Mod Power Syst Clean Energy 6(5):958–967
Parandehgheibi M, Modiano E (2013) Robustness of interdependent networks: the case of communication networks and the power grid[C]//2013 IEEE Global Communications Conference (GLOBECOM). IEEE, pp 2164–2169
Gong Yiyu W, Hao YK (2013) A network partition method for power system reactive power control based on power flow tracing. Autom Electric Power Syst 37(9):29–33
Tang Y, He H, Wen J et al (2015) Power system stability control for a wind farm based on adaptive dynamic programming. IEEE Trans Smart Grid 6(1):166–177
Zhenbo WEI (2015) Overview of complex networks community structure and its applications in electric power network analysis. Proc CSEE 35(7):1567–1577
Gaofeng PAN, Xinghua WANG, Xiangang PENG et al (2013) Study of power grid partition identification method based on community structure detection. Power Syst Prot Control 41(22):32–37
Gaofeng P, Xinghua W, Xiangang P et al (2013) Study of power grid partition identification method based on community structure detection. Power Syst Prot Control 41(13):116–121
Cai Y, Cao Y, Li Y et al (2016) Cascading failure analysis considering interaction between power grids and communication networks. IEEE Trans Smart Grid 7(1):530–538
Yijia CAO, Yudong ZHANG, Zhejing BAO (2013) Analysis of cascading failures under interactions between power grid and communication network. Electric Power Autom Equip 33(1):7–11
Qi L, Dou W, Wang W, Li G, Yu H, Wan S (2018) Dynamic mobile crowdsourcing selection for electricity load forecasting. IEEE Access 6:46926–46937
Gao H, Mao S, Huang W, Yang X (2018) Applying probabilistic model checking to financial production risk evaluation and control: a case study of Alibabas Yue Bao. IEEE Trans Comput Soc Syst 5(3):785–795
Qi L, Chen Y, Yuan Y, Fu S, Zhang X, Xu X (2019) A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems. World Wide Web. https://doi.org/10.1007/s11280-019-00684-y
Yin Y, Chen L, Xu Y, Wan J, Zhang H, Mai Z (2019) QoS prediction for service recommendation with deep feature learning in edge computing environment. Mobile Netw Appl. https://doi.org/10.1007/s11036-019-01241-7
Qi L, He Q, Chen F, Dou W, Wan S, Zhang X, Xu X (2019) Finding all you need: web APIs recommendation in web of things through keywords search. IEEE Trans Comput Soc Syst 6(5):1063–1072
Gao H, Zhang K, Yang J, Wu F, Liu H (2018) Applying improved particle swarm optimization for dynamic service composition focusing on quality of service evaluations under hybrid networks. Int J Distrib Sens Netw 14(2):1550147718761583
Ruihan S, Dichen L, Jie Z et al (2012) Weighted network model based recognition of dangerous lines under power flow transferring. Power Syst Technol 36(5):245–250
Yong L, Junyong L, Xiaoyu L et al (2013) Vulnerability assessment based on power flow entropy for lines in cascading failures and its application in Sichuan backbone power grid. Electr Power Autom Equip 33(10):40–46 (in Chinese)
Gao Z, Xuan HZ, Zhang H, Wan S, Choo KKR (2019) Adaptive fusion and category-level dictionary learning model for multi-view human action recognition. IEEE Int Things J. https://doi.org/10.1109/JIOT.2019.2911669
Yin Y, Yu F, Xu Y, Yu L, Mu J (2017) Network location-aware service recommendation with random walk in cyber-physical systems. Sensors 17(9):2059
Zhang Q, Wan S, Wang B, Gao DW, Ma H (2019) Anomaly detection based on random matrix theory for industrial power systems. J Syst Archit 95:67–74
Qinglai GUO, Shujun XIN, Hongbin SUN et al (2016) Power system cyber physical modeling and security assessment: motivation and challenges. Proc CSEE 36(6):1481–1489
Ding S, Qu S, Xi Y, Wan S (2019) Stimulus-driven and concept-driven analysis for image caption generation. Neurocomputing. https://doi.org/10.1016/j.neucom.2019.04.095
Xu X, Gu R, Dai F, Qi L, Wan S (2019) Multi-objective computation offloading for internet of vehicles in cloud-edge computing. Wireless Netw https://doi.org/10.1007/s11276-019-02127-y
Parshani R, Rozenblat C, Ietri D et al (2010) Inter-similarity between coupled networks. EPL 92(6):68002
Bordel B, Alcarria R, de Rivera DS, Robles T (2018) Process execution in cyber-physical systems using cloud and cyber-physical internet services. J Supercomput 74(8):4127–4169
Gao Z, Wang DY, Wan SH, Zhang H, Wang YL (2019) Cognitive-inspired class-statistic matching with triple-constrain for camera free 3D object retrieval. Future Gener Comput Syst 94:641–653
Wan S, Zhao Y, Wang T, Gu Z, Abbasi QH, Choo KKR (2019) Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things. Future Gener Comput Syst 91:382–391
Din FU, Ahmad A, Ullah H, Khan A, Umer T, Wan S (2019) Efficient sizing and placement of distributed generators in cyber-physical power systems. J Syst Archit 97:197–207
Zhang R, Xie P, Wang C, Liu G, Wan S (2019) Classifying transportation mode and speed from trajectory data via deep multi-scale learning. Comput Netw 162:106861
Wan S, Li X, Xue Y et al (2019) Efficient computation offloading for Internet of Vehicles in edge computing-assisted 5G networks. J Supercomput. https://doi.org/10.1007/s11227-019-03011-4
Acknowledgements
The authors are grateful for the Project (2018YFB0904200), supported by the key R&D Program of China, the Project (2019-ZJ-950Q), supported by the National Natural Science Foundation of Qinghai Province, and the Fundamental Research Funds for the Central Universities (No. 2722019PY052).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, B., Ma, H., Wang, X. et al. Vulnerability assessment method for cyber-physical system considering node heterogeneity. J Supercomput 76, 2622–2642 (2020). https://doi.org/10.1007/s11227-019-03027-w
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-019-03027-w