Abstract
Purpose of Review
In this paper, we study the literature on cyber-physical security of electrical power systems. The paper is intended to address the security strengths and weaknesses of the electrical power systems against malicious attacks.
Recent Findings
The concept of holistic resilience cycle (HRC) is introduced to improve cyber-physical security of electrical power systems. HRC is a systematic view to the security of the power systems, characterized by its four stages as closely interconnected and explicable only by reference to the whole. HRC includes four stages of prevention and planning, detection, mitigation and response, and system recovery.
Summary
Power systems are evolving from traditional settings towards more autonomous and smart grids. Cyber-physical security is critical for the safe and secure operations of the power systems. To achieve a higher security level for power systems, the research community should follow a systematic approach and consider all stages of the holistic resilience cycle in addressing security problems of the power systems.
Similar content being viewed by others
References
People of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
Smith R. Assault on california power station raises alarm on potential for terrorism. [Online]. 2014. Available: http://www.wsj.com/articles/.
Nezamoddini N, Mousavian S, Erol-Kantarci M. A risk optimization model for enhanced power grid resilience against physical attacks. Electr Power Syst Res. 2017;143:329–38. https://doi.org/10.1016/j.epsr.2016.08.046.
Mousavian S, Valenzuela J, Wang J. Real-time data reassurance in electrical power systems based on artificial neural networks. Electr Power Syst Res. 2013;96:285–95. https://doi.org/10.1016/j.epsr.2012.11.015.
Salmeron J, Wood K, Baldick R. Analysis of electric grid security under terrorist threat. IEEE Trans Power Syst. 2004;19(2):905–12. https://doi.org/10.1109/TPWRS.2004.825888.
Donde V, Lopez V, Lesieutre B, Pinar A, Yang C, Meza J. Identification of severe multiple contingencies in electric power networks. In Proceedings of the 37th Annual North American Power Symposium, 2005. IEEE. 2005.
Donde V, Lopez V, Lesieutre B, Pinar A, Yang C, Meza J. Severe multiple contingency screening in electric power systems. IEEE Trans Power Syst. 2008;23(2):406–17. https://doi.org/10.1109/TPWRS.2008.919243.
Brown G, Carlyle M, Salmeron J, Wood K. Defending critical infrastructure. Interfaces. 2006;36(6):530–44. https://doi.org/10.1287/inte.1060.0252.
Alguacil N, Delgadillo A, Arroyo JM. A trilevel programming approach for electric grid defense planning. Comput Oper Res. 2014;41:282–90. https://doi.org/10.1016/j.cor.2013.06.009.
Yao Y, Edmunds T, Papageorgiou D, Alvarez R. Trilevel optimization in power network defense. IEEE Trans Syst Man Cybern Part C Appl Rev. 2007;37:712–8.
Salmeron J, Wood K, Baldick R. Worst-case interdiction analysis of large-scale electric power grids. IEEE Trans Power Syst. 2009;24(1):96–104. https://doi.org/10.1109/TPWRS.2008.2004825.
Holmgren AJ, Jenelius E, Westin J. Evaluating strategies for defending electric power networks against antagonistic attack. IEEE Trans Power Syst. 2007;22(1):76–84. https://doi.org/10.1109/TPWRS.2006.889080.
Chen G, Dong ZY, Hill DJ, Xue YS. Exploring reliable strategies for defending power systems against targeted attacks. IEEE Trans Power Syst. 2011;26(3):1000–9. https://doi.org/10.1109/TPWRS.2010.2078524.
Cappanera P, Scaparra MP. Optimal allocation of protective resources in shortest-path networks. Transp Sci. 2011;45(1):64–80. https://doi.org/10.1287/trsc.1100.0340.
Ma CYT, Yau DK, Lou X, Rao NS. Markov game analysis for attack-defense of power networks under possible misinformation. IEEE Trans Power Syst. 2012;28(2):1676–86.
Donde V, Lopez V, Lesieutre B, Pinar A, Yang C, Meza J. Identification of severe multiple contingencies in electric power networks. In Proceedings of the IEEE 37th Annual North American Power Symposium. 2005. p. 59–66.
Pinar A, Reichert A, Lesieutre B. Computing criticality of lines in power systems. In IEEE International Symposium onCircuits and Systems. 2007. p. 65–68.
Correa GJ, Yusta JM. Grid vulnerability analysis based on scalefree graphs versus power flow models. Electr Power Syst Res. 2013;101:71–9. https://doi.org/10.1016/j.epsr.2013.04.003.
• Suo H, Wan J, Zou C, Liu J. Security in the internet of things: a review. In International Conference on Computer Science and Electronics Engineering, Hangzhou. 2012. p. 648–651. This review provides details on the state-of-the-art on cyber attack prevention technologies including encryption mechanisms, communication security, protecting sensor data, and cryptographic algorithms.
Liu Y, Ning P, Reiter MK. False data injection attacks against state estimation in electric power grids. In Proceedings of the 16th ACM conference on Computer and communications security. ACM. 2009. p. 21–32.
Li Y, Wang Y. State summation for detecting false data attack on smart grid. Int J Electr Power Energy Syst. 2014;57:156–63. https://doi.org/10.1016/j.ijepes.2013.11.057.
Li S, Yilmaz Y, Wang X. Quickest detection of false data injection attack in wide-area smart grids. IEEE Trans Smart Grid. 2015;6(6):2725–35. https://doi.org/10.1109/TSG.2014.2374577.
Moslemi R, Moslemi R, Velni JM. A fast, decentralized covariance selection-based approach to detect cyber attacks in smart grids. IEEE Trans Smart Grid, vol. PP. 2017; 1–1.
Liu T, Sun Y, Liu Y, Gui Y, Zhao Y, Wang D, et al. Abnormal traffic-indexed state estimation: a cyberphysical fusion approach for smart grid attack detection. Futur Gener Comput Syst. 2015;49:94–103. https://doi.org/10.1016/j.future.2014.10.002.
Khalid HM, Peng JC-H. Immunity toward data-injection attacks using multisensor track fusion-based model prediction. IEEE Trans Smart Grid. 2015;8:697–707.
Zhu S, Wu L, Mousavian S, Roh JH. An optimal joint placement of PMUs and flow measurements for ensuring power system observability under N-2 transmission contingencies. Int J Electr Power Energy Syst. 2018;95:254–65. https://doi.org/10.1016/j.ijepes.2017.08.025.
Mousavian S, Valenzuela J, Wang J. A two-phase investment model for optimal allocation of phasor measurement units considering transmission switching. Electr Power Syst Res. 2015;119:492–8. https://doi.org/10.1016/j.epsr.2014.10.025.
Mousavian S, Feizollahi MJ. An investment decision model for the optimal placement of phasor measurement units. Expert Syst Appl. 2015;42(21):7276–84. https://doi.org/10.1016/j.eswa.2015.05.041.
Zhao J, Zhang G, Jabr RA. Robust detection of cyber attacks on state estimators using phasor measurements. IEEE Trans Power Syst. 2017;32(3):2468–70. https://doi.org/10.1109/TPWRS.2016.2603447.
Deng R, Zhuang P, Liang H. Ccpa: coordinated cyberphysical attacks and countermeasures in smart grid. IEEE Trans Smart Grid. vol. PP. 2017; 1–1.
Li B, Lu R, Wang W, Choo K-KR. Distributed host-based collaborative detection for false data injection attacks in smart grid cyber-physical system. Journal of Parallel and Distributed Computing. 2017;103:32–41. https://doi.org/10.1016/j.jpdc.2016.12.012.
Waghmare S, Kazi F, Singh N. Data driven approach to attack detection in a cyber-physical smart grid system. In Indian Control Conference (ICC). IEEE. 2017.
Maglaras LA, Jiang J, Cruz TJ. Combining ensemble methods and social network metrics for improving accuracy of ocsvm on intrusion detection in scada systems. Journal of Information Security and Applications. 2016;30:15–26. https://doi.org/10.1016/j.jisa.2016.04.002.
He Y, Mendis GJ, Wei J. Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism. IEEE Trans Smart Grid, vol. PP. 2017; 1–1.
Anwar A, Mahmood AN, Pickering M. Modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements. J Comput Syst Sci. 2017;83(1):58–72. https://doi.org/10.1016/j.jcss.2016.04.005.
Ashok A, Govindarasu M, Ajjarapu V. Online detection of stealthy false data injection attacks in power system state estimation. Online Detection of Stealthy False Data Injection Attacks in Power System State Estimation, vol. PP. 2016; 1–1.
Mohammadpourfard M, Sami A, Seifi AR. A statistical unsupervised method against false data injection attacks: a visualization-based approach. Expert Syst Appl. 2017;84:242–61. https://doi.org/10.1016/j.eswa.2017.05.013.
Yang W, Lei L, Yang C. Event-based distributed state estimation under deception attack. Neurocomputing, vol. PP. 2017; 1–1.
• Mousavian S, Valenzuela J, Wang J. A probabilistic risk mitigation model for cyber-attacks to pmu networks. IEEE Trans Power Systems. 2015. The authors investigated a probabilistic risk mitigation response to cyber attacks to PMU networks after detection of the attack. The article is the first one in the literature that addressed how to respond to cyber attacks to power systems after detection of the attack;30(1):156–65. https://doi.org/10.1109/TPWRS.2014.2320230.
Mousavian S, Erol-Kantarci M, Ortmeyer T. Cyber attack protection for a resilient electric vehicle infrastructure. San Diego: IEEE Globecom Workshops (GC Wkshps); 2015. p. 1–6.
Mousavian S, Erol-Kantarci M, Wu L, Ortmeyer T. A riskbased optimization model for electric vehicle infrastructure response to cyber attacks. IEEE Trans Smart Grid. PP(99); s1–1.
Lin H, Chen C, Wang J, Qi J, Jin D, Kalbarczyk Z, Iyer RK. Self-healing attack-resilient pmu network for power system operation. IEEE Transactions on Smart Grid, vol. PP. 2016; 1–1.
Yuan Y, Li Z, Ren K. Modeling load redistribution attacks in power systems. IEEE Transactions on Smart Grid. 2011;2(2):382–90. https://doi.org/10.1109/TSG.2011.2123925.
Yuan Y, Li Z, Ren K, Quantitative analysis of load redistribution attacks in power systems. IEEE Transactions on Parallel and Distributed Systems. 2012;23(9):1731-38. https://doi.org/10.1109/TPDS.2012.58.
Liu X, Li Z. Local load redistribution attacks in power systems with incomplete network information. IEEE Transactions on Smart Grid. 2014;5(4):1665–76. https://doi.org/10.1109/TSG.2013.2291661.
Xiang Y, Wang L. A game-theoretic approach to optimal defense strategy against load redistribution attack. In IEEE Power & Energy Society General Meeting. IEEE. 2015.
Xiang Y, Ding Z, Zhang Y, Wang L. Power system reliability evaluation considering load redistribution attacks. IEEE Transactions on Smart Grid. 2017;8:889–901.
Wang K, Du M, Maharjan S, Sun Y. Strategic honeypot game model for distributed denial of service attacks in the smart grid. IEEE Transactions on Smart Grid. 2017;8(5):2474–82. https://doi.org/10.1109/TSG.2017.2670144.
Diovu RC, Agee JT. A cloud-based openflow firewall for mitigation against ddos attacks in smart grid ami networks. In PowerAfrica, 2017 I.E. PES. IEEE. 2017.
Lu W-Z, Gu W-X, Yu S-Z. One-way queuing delay measurement and its application on detecting ddos attack. J Netw Comput Appl. 2009;32(2):367–76. https://doi.org/10.1016/j.jnca.2008.02.018.
Varalakshmi P, Selvi ST. Thwarting ddos attacks in grid using information divergence. Futur Gener Comput Syst. 2013;29(1):429–41. https://doi.org/10.1016/j.future.2011.10.012.
Srikantha P, Kundur D. Denial of service attacks and mitigation for stability in cyber-enabled power grid. In 2015 I.E. Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE. 2015.
Liu H, Chen Y, Chuah MC, Yang J, Poor HV. Enabling self-healing smart grid through jamming resilient local controller switching. IEEE Transactions on Dependable and Secure Computing. 2015;14:377–91.
Chlela M, Mascarella D, Joos G, Kassouf M. Fallback control for isochronous energy storage systems in autonomous microgrids under denial-of-service cyber-attacks. IEEE Trans Smart Grid, vol. PP. 2017; 1–1.
Salinas S, Li M, Li P. Privacy-preserving energy theft detection in smart grids: a p2p computing approach. IEEE Journal on Selected Areas in Communications. 2013;31(9):257–67. https://doi.org/10.1109/JSAC.2013.SUP.0513023.
Jiang R, Lu R, Wang Y, Luo J, Shen C, Shen XS. Energy-theft detection issues for advanced metering infrastructure in smart grid. Tsinghua Sci Technol. 2014;19(2):105–20. https://doi.org/10.1109/TST.2014.6787363.
Pasdar A, Mirzakuchaki S. A solution to remote detecting of illegal electricity usage based on smart metering. In 2nd International Workshop on Soft Computing Applications, 2007. SOFA. IEEE. 2007.
Deb S, Bhowmik PK, Paul A. Remote detection of illegal electricity usage employing smart energy meter—a current based technique. In IEEE PES Innovative Smart Grid Technologies—India (ISGT India). IEEEx. 2011.
Bat-Erdene B, Lee B, Kim M-Y, Ahn T, Kim D. Extended smart meters-based remote detection method for illegal electricity usage. IET Generation, Transmission & Distribution. 2013;7(11):1332–43. https://doi.org/10.1049/iet-gtd.2012.0287.
McLaughlin S, Holbert B, Zonouz S, Berthier R. Amids: a multi-sensor energy theft detection framework for advanced metering infrastructures. In Third International Conference on Third International Conference on, 2012.
Jokar P, Arianpoo N, Leung VCM. Electricity theft detection in ami using customers consumption patterns. IEEE Transactions on Smart Grid. 2016;7:2016–226.
Villar-Rodriguez E, Ser JD, Oregi I, Bilbao MN, Gil-Lopez S. Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis. Energy. 2017;137:118–28. https://doi.org/10.1016/j.energy.2017.07.008.
Nagi J, Yap KS, Tiong SK, Ahmed SK, Mohammad AM. Detection of abnormalities and electricity theft using genetic support vector machines. In IEEE Region 10 Conference TENCON 2008. IEEE. 2008.
Depuru SSSR, Wang L, Devabhaktuni V. Support vector machine based data classification for detection of electricity theft. In IEEE/PES Power Systems Conference and Exposition (PSCE). IEEE. 2011.
Depuru SSSR, Wang L, Devabhaktuni V, Nelapati P. A hybrid neural network model and encoding technique for enhanced classification of energy consumption data. In IEEE Power and Energy Society General Meeting. IEEE. 2011.
Jindal A, Dua A, Kaur K. Decision tree and svm-based data analytics for theft detection in smart grid. IEEE Transactions on Industrial Informatics. 2016;12(3):1005–16. https://doi.org/10.1109/TII.2016.2543145.
Glauner P, Boechat A, Dolberg L, State R, Bettinger F, Rangoni Y, Duarte D. Large-scale detection of non-technical losses in imbalanced data sets. In IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE. 2016.
Ghanbari M, Kinsner W, Ferens K. Anomaly detection in a smart grid using wavelet transform, variance fractal dimension and an artificial neural network. In IEEE Electrical Power and Energy Conference (EPEC). IEEE. 2016.
Sargolzaei A, Yen K, Abdelghani M. Delayed inputs attack on load frequency control in smart grid. In IEEE PES Innovative Smart Grid Technologies Conference (ISGT). 2014.
Sargolzaei A, Yen KK, Abdelghani MN. Preventing time-delay switch attack on load frequency control in distributed power systems. IEEE Transactions on Smart Grid. 2016;7:1176–85.
Shafique M, Iqbal N. Load frequency resilient control of power system against delayed input cyber attack. In Symposium on Recent Advances in Electrical Engineering (RAEE). IEEE. 2015.
Sargolzaei A, Yen KK, Abdelghani M, Sargolzaei S, Car-bunar B. Resilient design of networked control systems under time delay switch attacks, application in smart grid. IEEE Access, vol. PP. 2017; 1–1.
Piro C, Shields C, Levine BN. Detecting the sybil attack in mobile ad hoc networks. In Securecomm and Workshops. IEEE. 2006.
Lv S, Wang X, Zhao X, Zhou X. Detecting the sybil attack cooperatively in wireless sensor networks. In International Conference on Computational Intelligence and Security, 2008. CIS ‘08. IEEE. 2008.
Rabieh K, Mahmoud MMEA, Guo TN, Younis M. Cross-layer scheme for detecting large-scale colluding sybil attack in vanets. In IEEE International Conference on Communications (ICC). IEEE. 2015.
Sharma AK, Saroj SK, Chauhan SK, Saini SK. Sybil attack prevention and detection in vehicular ad hoc network. In International Conference on Computing, Communication and Automation (ICCCA). IEEE. 2016.
Sarigiannidis P, Karapistoli E, Economides AA. Detecting sybil attacks in wireless sensor networks using uwb ranging-based information. Expert Syst Appl. Nov. 2015;42(21):7560–72. https://doi.org/10.1016/j.eswa.2015.05.057.
Hoehn A, Zhang P. Detection of replay attacks in cyberphysical systems. In American Control Conference (ACC). IEEE. 2016.
Misra S, Tayeen ASM, Xu W. Sybilexposer: an effective scheme to detect sybil communities in online social networks. In IEEE International Conference on Communications (ICC). IEEE. 2016.
Gu P, Khatoun R, Begriche Y, Serhrouchni A. Vehicle driving pattern based sybil attack detection. In IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE. 2016.
Irita T, Namerikawa T. Detection of replay attack on smart grid with code signal and bargaining game. In 2017 American Control Conference (ACC). IEEE. 2017.
•• Mohsenian-Rad A-H, Leon-Garcia A. Distributed internet-based load altering attacks against smart power grids. IEEE Transactions on Smart Grid. 2011. The article introduces indirect cyber attacks to power systems taking advantage of the mutual dependency of smart grids and IoT;2(4):667–74. https://doi.org/10.1109/TSG.2011.2160297.
Dvorkin Y, Garg S. Iot-enabled distributed cyber-attacks on transmission and distribution grids. In Proceedings of the 49th North American Power Symposium (NAPS). 2017.
Amini S, Mohsenian-Rad H, Pasqualetti F. Dynamic load altering attacks in smart grid. In 2015 I.E. Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE. 2015.
Amini S, Pasqualetti F, Mohsenian-Rad H. Dynamic load altering attacks against power system stability: attack models and protection schemes. IEEE Trans Smart Grid. 2016;99:1. https://doi.org/10.1109/TSG.2016.2622686.
Amini S, Pasqualetti F, Mohsenian-Rad H. Detecting dynamic load altering attacks: a data-driven time- frequency analysis. In 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm), Miami, FL; 2015. p 503-8. https://doi.org/10.1109/SmartGridComm.2015.7436350.
Baer WS, Hassell S, Vollaar BA. Electricity requirements for a digital society. Santa Monica, Tech. Rep.: RAND Corporation; 2002.
Armbrust M, Fox A, Griffith R, Joseph AD, Katz RH, Konwinski A, Lee G, Patterson DA, Rabkin A, Stoica I, Zaharia M. Above the clouds: a berkeley view of cloud computing. University of California, Berkeley, Tech. Rep. 2009.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflicts of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Additional information
This article is part of the topical collection on Energy Markets
Rights and permissions
About this article
Cite this article
Mehrdad, S., Mousavian, S., Madraki, G. et al. Cyber-Physical Resilience of Electrical Power Systems Against Malicious Attacks: a Review. Curr Sustainable Renewable Energy Rep 5, 14–22 (2018). https://doi.org/10.1007/s40518-018-0094-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40518-018-0094-8