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A survey of anomaly detection methods for power grids

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Abstract

The power grid is a constant target for attacks as they have the potential to affect a large geographical location, thus affecting hundreds of thousands of customers. With the advent of wireless sensor networks in the smart grids, the distributed network has more vulnerabilities than before, giving numerous entry points for an attacker. The power grid operation is usually not hindered by small-scale attacks; it is popularly known to be self-healing and recovers from an attack as the neighboring areas can mitigate the loss and prevent cascading failures. However, the attackers could target users, admins and other control personnel, disabling access to their systems and causing a delay in the required action to be taken. Termed as the biggest machine in the world, the US power grid has only been having an increased risk of outages due to cyber attacks. This work focuses on structuring the attack detection literature in power grids and provides a systematic review and insights into the work done in the past decade in the area of anomaly or attack detection in the domain.

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References

  1. Abdel-Basset, M., Moustafa, N., Hawash, H.: Privacy-preserved generative network for trustworthy anomaly detection in smart grids: a federated semi-supervised approach. IEEE Trans. Ind. Inf. 19(1), 995–1005 (2022)

    Article  Google Scholar 

  2. Abdelkhalek, M., Ravikumar, G., Govindarasu, M.: ML-based anomaly detection system for der communication in smart grid. In: 2022 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (ISGT), IEEE, pp. 1–5 (2022)

  3. Ahmed, A., Sajan, K.S., Srivastava, A., Wu, Y.: Anomaly detection, localization and classification using drifting synchrophasor data streams. IEEE Trans. Smart Grid 12(4), 3570–3580 (2021)

    Article  Google Scholar 

  4. Ahmed, C.M., MR, G.R., Mathur, A.P.: Challenges in machine learning based approaches for real-time anomaly detection in industrial control systems. In: 2020 6th ACM on Cyber-physical System Security Workshop, pp. 23–29 (2020)

  5. Al-Abassi, A., Sakhnini, J., Karimipour, H.: Unsupervised stacked autoencoders for anomaly detection on smart cyber-physical grids. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp. 3123–3129 (2020)

  6. Albaseer, A., Abdallah, M.: Privacy-preserving honeypot-based detector in smart grid networks: A new design for quality-assurance and fair incentives federated learning framework. In: 2023 IEEE 20th Consumer Communications and Networking Conference (CCNC), IEEE, pp. 722–727 (2023)

  7. Aligholian, A., Farajollahi, M., Mohsenian-Rad, H.: Unsupervised learning for online abnormality detection in smart meter data. In: 2019 Power and Energy Society General Meeting (PESGM), IEEE, pp. 1–5 (2019)

  8. Alkuwari, A.N., Al-Kuwari, S., Qaraqe, M.: Anomaly detection in smart grids: a survey from cybersecurity perspective. In: 2022 3rd International Conference on Smart Grid and Renewable Energy (SGRE), IEEE, pp. 1–7 (2022)

  9. Allnutt, J., Anand, D., Arnold, D., Goldstein, A., Li-Baboud, Y.S., Martin, A., Nguyen, C., Noseworthy, R., Subramaniam, R., Weiss, M.: Timing challenges in the smart grid. NIST Spec. Publ. 1500, 08 (2017)

    Google Scholar 

  10. Anwar, A., Mahmood, A.N.: Cyber security of smart grid infrastructure. arXiv preprint arXiv:1401.3936 [cs.CR] (2014)

  11. Anwar, A., Mahmood, A.N.: Anomaly detection in electric network database of smart grid: graph matching approach. Electr. Power Syst. Res. 133, 51–62 (2016)

    Article  Google Scholar 

  12. Araya, D.B., Grolinger, K., ElYamany, H.F., Capretz, M.A., Bitsuamlak, G.: Collective contextual anomaly detection framework for smart buildings. In: 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 511–518 (2016)

  13. Araya, D.B., Grolinger, K., ElYamany, H.F., Capretz, M.A., Bitsuamlak, G.: An ensemble learning framework for anomaly detection in building energy consumption. Energy Build. 144, 191–206 (2017)

    Article  Google Scholar 

  14. Arjunan, P., Khadilkar, H.D., Ganu, T., Charbiwala, Z.M., Singh, A., Singh, P.: Multi-user energy consumption monitoring and anomaly detection with partial context information. In: 2015 2nd ACM International Conference on Embedded Systems for Energy-efficient Built Environments, pp. 35–44 (2015)

  15. Atalay, M., Angin, P.: A digital twins approach to smart grid security testing and standardization. In: 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT, IEEE, pp. 435–440 (2020)

  16. Azad, S., Sabrina, F., Wasimi, S.: Transformation of smart grid using machine learning. In: 2019 29th Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1–6 (2019)

  17. Azizi, E., Beheshti, M.T., Bolouki, S.: Appliance-level anomaly detection in nonintrusive load monitoring via power consumption-based feature analysis. IEEE Trans. Consum. Electron. 67(4), 363–371 (2021)

    Article  Google Scholar 

  18. Badrinath Krishna, V., Iyer, R.K., Sanders, W.H.: ARIMA-based modeling and validation of consumption readings in power grids. In: 2015 International Conference on Critical Information Infrastructures Security, Springer, pp. 199–210 (2015)

  19. Baig, Z.A.: On the use of pattern matching for rapid anomaly detection in smart grid infrastructures. In: 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), IEEE, pp. 214–219 (2011)

  20. Barua, A., Muthirayan, D., Khargonekar, P.P., Al Faruque, M.A.: Hierarchical temporal memory based machine learning for real-time, unsupervised anomaly detection in smart grid: WiP abstract. In: 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), IEEE, pp. 188–189 (2020)

  21. Barua, A., Muthirayan, D., Khargonekar, P.P., Al Faruque, M.A.: Hierarchical temporal memory based one-pass learning for real-time anomaly detection and simultaneous data prediction in smart grids. IEEE Trans. Dependable Secure Comput. 19(3), 1770–1782 (2020)

    Article  Google Scholar 

  22. Basumallik, S., Ma, R., Eftekharnejad, S.: Packet-data anomaly detection in PMU-based state estimator using convolutional neural network. Int. J. Electr. Power Energy Syst. 107, 690–702 (2019)

    Article  Google Scholar 

  23. Belhadi, A., Djenouri, Y., Srivastava, G., Jolfaei, A., Lin, J.C.W.: Privacy reinforcement learning for faults detection in the smart grid. Ad Hoc Netw. 119, 102,541 (2021)

    Article  Google Scholar 

  24. Bellala, G., Marwah, M., Arlitt, M., Lyon, G., Bash, C.E.: Towards an understanding of campus-scale power consumption. In: 2011 3rd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, pp. 73–78 (2011)

  25. Boumkheld, N., Panda, S., Rass, S., Panaousis, E.: Honeypot type selection games for smart grid networks. In: Decision and Game Theory for Security: 10th International Conference, GameSec 2019, Stockholm, Sweden, October 30–November 1, 2019, Proceedings 10, Springer, pp. 85–96 (2019)

  26. Case, D.U.: Analysis of the cyber attack on the ukrainian power grid. Electricity Information Sharing and Analysis Center (E-ISAC) 388 (2016)

  27. Chahla, C., Snoussi, H., Merghem, L., Esseghir, M.: A novel approach for anomaly detection in power consumption data. In: 2019 8th International Conference on Pattern Recognition Applications and Method (ICPRAM), pp. 483–490 (2019)

  28. Chahla, C., Snoussi, H., Merghem, L., Esseghir, M.: A deep learning approach for anomaly detection and prediction in power consumption data. Energ. Effic. 13(8), 1633–1651 (2020)

    Article  Google Scholar 

  29. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15:1-15:58 (2009)

    Article  Google Scholar 

  30. Chen, H., Fei, X., Wang, S., Lu, X., Jin, G., Li, W., Wu, X.: Energy consumption data based machine anomaly detection. In: 2014 2nd International Conference on Advanced Cloud and Big Data, IEEE, pp. 136–142 (2014)

  31. Chen, P.Y., Yang, S., McCann, J.A.: Distributed real-time anomaly detection in networked industrial sensing systems. IEEE Trans. Ind. Electron. 62(6), 3832–3842 (2014)

    Article  Google Scholar 

  32. Chen, T.M.: Stuxnet, the real start of cyber warfare? IEEE Network, Editor’s Note (2011)

  33. Chen, Z., Chen, D., Zhang, X., Yuan, Z., Cheng, X.: Learning graph structures with transformer for multivariate time series anomaly detection in IoT. IEEE Internet Things J. 9(12), 9179–9189 (2021)

    Article  Google Scholar 

  34. Chou, J.S., Telaga, A.S.: Real-time detection of anomalous power consumption. Renew. Sustain. Energy Rev. 33, 400–411 (2014)

    Article  Google Scholar 

  35. Cobb, P.: German steel mill meltdown: rising stakes in the internet of things. (2015) https://securityintelligence.com/german-steel-mill-meltdown-rising-stakes-in-the-internet-of-things/. Accessed 01 March 2023

  36. Cui, L., Qu, Y., Xie, G., Zeng, D., Li, R., Shen, S., Yu, S.: Security and privacy-enhanced federated learning for anomaly detection in iot infrastructures. IEEE Trans. Ind. Inf. 18(5), 3492–3500 (2021)

    Article  Google Scholar 

  37. Cui, M., Wang, J., Yue, M.: Machine learning-based anomaly detection for load forecasting under cyberattacks. IEEE Trans. Smart Grid 10(5), 5724–5734 (2019)

    Article  Google Scholar 

  38. Cui, S., Han, Z., Kar, S., Kim, T.T., Poor, H.V., Tajer, A.: Coordinated data-injection attack and detection in the smart grid: a detailed look at enriching detection solutions. IEEE Signal Process. Mag. 29(5), 106–115 (2012)

    Article  Google Scholar 

  39. Cui, W., Wang, H.: A new anomaly detection system for school electricity consumption data. Information 8(4), 151 (2017)

    Article  Google Scholar 

  40. Cultice, T., Ionel, D., Thapliyal, H.: Smart home sensor anomaly detection using convolutional autoencoder neural network. In: 2020 IEEE International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS), IEEE, pp. 67–70 (2020)

  41. Dabrowski, A., Ullrich, J., Weippl, E.R.: Grid shock: Coordinated load-changing attacks on power grids: The non-smart power grid is vulnerable to cyber attacks as well. In: 2017 33rd Annual Computer Security Applications Conference (ACSAC), pp. 303–314 (2017)

  42. Dai, H., Sun, X., Li, J., Zhang, G., Ji, X., Xu, W.: Power consumption-based anomaly detection for relay protection. In: 2020 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), IEEE, pp. 1139–1143 (2020)

  43. Danilczyk, W., Sun, Y.L., He, H.: Smart grid anomaly detection using a deep learning digital twin. In: 2020 52nd North American Power Symposium (NAPS), IEEE, pp. 1–6 (2021)

  44. Dey, M., Rana, S.P., Simmons, C.V., Dudley, S.: Solar farm voltage anomaly detection using high-resolution \(\mu \)PMU data-driven unsupervised machine learning. Appl. Energy 303, 117656 (2021)

    Article  Google Scholar 

  45. Dilraj, M., Nimmy, K., Sankaran, S.: Towards behavioral profiling based anomaly detection for smart homes. In: 2019 IEEE Region 10 Conference (TENCON), IEEE, pp. 1258–1263 (2019)

  46. Dong, X., Lin, H., Tan, R., Iyer, R.K., Kalbarczyk, Z.: Software-defined networking for smart grid resilience: Opportunities and challenges. In: Proceedings of the 1st ACM Workshop on Cyber-physical System Security, pp. 61–68 (2015)

  47. Drakontaidis, S., Stanchi, M., Glazer, G., Hussey, J., Leger, A.S., Matthews, S.J.: Towards energy-proportional anomaly detection in the smart grid. In: 2018 High Performance Extreme Computing Conference (HPEC), IEEE, pp. 1–7 (2018)

  48. Efstathopoulos, G., Grammatikis, P.R., Sarigiannidis, P., Argyriou, V., Sarigiannidis, A., Stamatakis, K., Angelopoulos, M.K., Athanasopoulos, S.K.: Operational data based intrusion detection system for smart grid. In: 2019 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), IEEE, pp. 1–6 (2019)

  49. El-Awadi, R., Fernández-Vilas, A., Redondo, R.P.D.: Fog computing solution for distributed anomaly detection in smart grids. In: 2019 International Conference on Wireless and Mobile Computing, pp. 348–353. Networking and Communications (WiMob), IEEE (2019)

  50. El Chamie, M., Lore, K.G., Shila, D.M., Surana, A.: Physics-based features for anomaly detection in power grids with micro-pmus. In: 2018 International Conference on Communications (ICC), IEEE, pp. 1–7 (2018)

  51. Elmasry, W., Wadi, M.: Detection of faults in electrical power grids using an enhanced anomaly-based method. Arab. J. Sci. Eng. 47, 14,899-14,914 (2022)

    Article  Google Scholar 

  52. Elmrabit, N., Zhou, F., Li, F., Zhou, H.: Evaluation of machine learning algorithms for anomaly detection. In: 2020 International Conference on Cyber Security and Protection of Digital Services, IEEE, pp. 1–8 (2020)

  53. Enerdynamics. What is a phasor measurement unit and how does it make the grid more reliable? (2021) https://www.enerdynamics.com/Energy-Currents_Blog/What-Is-a-Phasor-Measurement-Unit-and-How-Does-it-Make-the-Grid-More-Reliable.aspx. Accessed 01 March 2023

  54. Esmalifalak, M., Liu, L., Nguyen, N., Zheng, R., Han, Z.: Detecting stealthy false data injection using machine learning in smart grid. IEEE Syst. J. 11(3), 1644–1652 (2014)

    Article  Google Scholar 

  55. Falliere, N., Murchu, L.O., Chien, E.: W32.stuxnet dossier, version 1.4. Symantec Security Response (2011)

  56. Feng, L., Xu, S., Zhang, L., Wu, J., Zhang, J., Chu, C., Wang, Z., Shi H: Anomaly detection for electricity consumption in cloud computing: Framework, methods, applications, and challenges. EURASIP J. Wirel. Commun. Netw. 1, 1–12 (2020a)

  57. Feng, Z., Huang, J., Tang, W.H., Shahidehpour, M.: Data mining for abnormal power consumption pattern detection based on local matrix reconstruction. Int. J. Electr. Power Energy Syst. 123, 106315 (2020b)

  58. Fengming, Z., Shufang, L., Zhimin, G., Bo, W., Shiming, T., Mingming, P.: Anomaly detection in smart grid based on encoder-decoder framework with recurrent neural network. J. China Univ. Posts Telecommun. 24(6), 67–73 (2017)

    Article  Google Scholar 

  59. Fenza, G., Gallo, M., Loia, V.: Drift-aware methodology for anomaly detection in smart grid. IEEE Access 7, 9645–9657 (2019)

    Article  Google Scholar 

  60. Garg, S., Kaur, K., Batra, S., Kaddoum, G., Kumar, N., Boukerche, A.: A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications. Future Gener. Comput. Syst. 104, 105–118 (2020)

    Article  Google Scholar 

  61. Ghanbari, M., Kinsner, W., Ferens, K.: Anomaly detection in a smart grid using wavelet transform, variance fractal dimension and an artificial neural network. In: 2016 Electrical Power and Energy Conference (EPEC), IEEE, pp. 1–6 (2016)

  62. Ghanim, J., Issa, M., Awad, M.: An asymmetric loss with anomaly detection LSTM framework for power consumption prediction. In: 2022 21st Mediterranean Electrotechnical Conference (MELECON), IEEE, pp. 819–824 (2022)

  63. Gholami, A., Srivastava, A.K.: Comparative analysis of ml techniques for data-driven anomaly detection, classification and localization in distribution system. In: 2020 52nd North American Power Symposium (NAPS), IEEE, pp. 1–6 (2021)

  64. Grammatikis, P.R., Sarigiannidis, P., Sarigiannidis, A., Margounakis, D., Tsiakalos, A., Efstathopoulos, G.: An anomaly detection mechanism for IEC 60870-5-104. In: 2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST), IEEE, pp. 1–4 (2020)

  65. Graß, A., Beecks, C., Soto, J.A.C.: Unsupervised anomaly detection in production lines. In: Machine Learning for Cyber Physical Systems, Springer, pp. 18–25 (2019)

  66. Group CCESGC. Smart grid reference architecture (2012)

  67. Gunduz, M.Z., Das, R.: Cyber-security on smart grid: threats and potential solutions. Comput. Netw. 169(107), 094 (2020)

    Google Scholar 

  68. Hannon, C., Deka, D., Jin, D., Vuffray, M., Lokhov, A.Y.: Real-time anomaly detection and classification in streaming PMU data. In: 2021 Madrid PowerTech, IEEE, pp. 1–6 (2021)

  69. Haque, N.I., Shahriar, M.H., Dastgir, M.G., Debnath, A., Parvez, I., Sarwat, A., Rahman, M.A.: Machine learning in generation, detection, and mitigation of cyberattacks in smart grid: a survey. (2020) arXiv preprint arXiv:2010.00661

  70. He, Z., Raghavan, A., Hu, G., Chai, S., Lee, R.: Power-grid controller anomaly detection with enhanced temporal deep learning. In: 2019 18th IEEE International Conference On Trust, Security And Privacy in Computing And Communications/13th IEEE International Conference On Big Data Science and Engineering (TrustCom/BigDataSE), IEEE, pp. 160–167 (2019)

  71. Himeur, Y., Ghanem, K., Alsalemi, A., Bensaali, F., Amira, A.: Anomaly detection of energy consumption in buildings: a review, current trends and new perspectives. Appl. Energy 287(116), 601 (2020)

    Google Scholar 

  72. Himeur, Y., Alsalemi, A., Bensaali, F., Amira, A.: Smart power consumption abnormality detection in buildings using micromoments and improved K-nearest neighbors. Int. J. Intell. Syst. 36(6), 2865–2894 (2021)

    Article  Google Scholar 

  73. Himeur, Y., Ghanem, K., Alsalemi, A., Bensaali, F., Amira, A.: Artificial intelligence based anomaly detection of energy consumption in buildings: a review, current trends and new perspectives. Appl. Energy 287, 116,601 (2021)

    Article  Google Scholar 

  74. Hong, J., Liu, C.C., Govindarasu, M.: Integrated anomaly detection for cyber security of the substations. IEEE Trans. Smart Grid 5(4), 1643–1653 (2014)

    Article  Google Scholar 

  75. Hooi, B., Eswaran, D., Song, H.A., Pandey, A., Jereminov, M., Pileggi, L., Faloutsos, C.: Gridwatch: sensor placement and anomaly detection in the electrical grid. In: 2018 Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, pp. 71–86 (2018)

  76. Hosseinzadehtaher, M., Khan, A., Shadmand, M.B., Abu-Rub, H.: Anomaly detection in distribution power system based on a condition monitoring vector and ultra-short demand forecasting. In: 2020 CyberPELS (CyberPELS), IEEE, pp. 1–6 (2020)

  77. Hou, R., Pan, M., Zhao, Y., Yang, Y.: Image anomaly detection for IoT equipment based on deep learning. J. Vis. Commun. Image Represent. 64, 102,599 (2019)

    Article  Google Scholar 

  78. Hu, Y., Yang, A., Li, H., Sun, Y., Sun, L.: A survey of intrusion detection on industrial control systems. Int. J. Distrib. Sens. Netw. 14(8), 1550147718794,615 (2018)

    Article  Google Scholar 

  79. Huang, C.C., Tsao, Y.T., Hsu, J.Y.J: Abnormality detection by model-based estimation of power consumption. In: 2012 5th International Conference on Service-Oriented Computing and Applications (SOCA), IEEE, pp. 1–6 (2012)

  80. Huo, X., Lv, C., Pei, P., Gao, M., Wang, L.: Smart grid communication network traffic anomaly detection based on entropy analysis. In: 2016 2nd International Conference on Computer and Communications (ICCC), IEEE, pp. 1082–1086 (2016)

  81. Huong, T.T., Bac, T.P., Long, D.M., Luong, T.D., Dan, N.M., Thang, B.D., Tran, K.P.: Detecting cyberattacks using anomaly detection in industrial control systems: A federated learning approach. Comput. Ind. 132(103), 509 (2021)

    Google Scholar 

  82. Ibrahim, M., Alsheikh, A., Awaysheh, F.M., Alshehri, M.D.: Machine learning schemes for anomaly detection in solar power plants. Energies 15(3), 1082 (2022)

    Article  Google Scholar 

  83. Ishimaki, Y., Bhattacharjee, S., Yamana, H., Das, S.K.: Towards privacy-preserving anomaly-based attack detection against data falsification in smart grid. In: 2020 International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, pp. 1–6 (2020)

  84. Jafari, M., Kavousi-Fard, A., Chen, T., Karimi, M.: A review on digital twin technology in smart grid, transportation system and smart city: challenges and future. IEEE Access (2023)

  85. Jaiswal, R., Chakravorty, A., Rong, C.: Distributed fog computing architecture for real-time anomaly detection in smart meter data. In: 2020 6th International Conference on Big Data Computing Service and Applications (BigDataService), IEEE, pp. 1–8 (2020)

  86. Jamei, M., Scaglione, A., Roberts, C., Stewart, E., Peisert, S., McParland, C., McEachern, A.: Anomaly detection using optimally placed \(\mu \)PMU sensors in distribution grids. IEEE Trans. Power Syst. 33(4), 3611–3623 (2017)

    Article  Google Scholar 

  87. Janetzko, H., Stoffel, F., Mittelstädt, S., Keim, D.A.: Anomaly detection for visual analytics of power consumption data. Comput. Graph. 38, 27–37 (2014)

    Article  Google Scholar 

  88. Jung, O., Smith, P., Magin, J., Reuter, L.: Anomaly detection in smart grids based on software defined networks. In: 2019 8th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), pp. 157–164 (2019)

  89. Kabir-Querrec, M., Mocanu, S., Bellemain, P., Thiriet, J.M., Savary, E.: Corrupted goose detectors: anomaly detection in power utility real-time ethernet communications. GreHack 2015, 1–9 (2015)

    Google Scholar 

  90. Karimipour, H., Leung, H.: Relaxation-based anomaly detection in cyber-physical systems using ensemble Kalman filter. IET Cyber-Phys. Syst.: Theory Appl. 5(1), 49–58 (2020)

    Article  Google Scholar 

  91. Karimipour, H., Dehghantanha, A., Parizi, R.M., Choo, K.K.R., Leung, H.: A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids. IEEE Access 7, 80,778-80,788 (2019)

    Article  Google Scholar 

  92. Karimipour, H., Geris, S., Dehghantanha, A., Leung, H.: Intelligent anomaly detection for large-scale smart grids. In: 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), IEEE, pp. 1–4 (2019)

  93. Khaledian, E., Pandey, S., Kundu, P., Srivastava, A.K.: Real-time synchrophasor data anomaly detection and classification using isolation forest, k means, and loop. IEEE Trans. Smart Grid 12(3), 2378–2388 (2020)

    Article  Google Scholar 

  94. Kim, Y., Hakak, S., Ghorbani, A.: Smart grid security: attacks and defence techniques. IET Smart Grid (2022)

  95. Korba, A.A., Tamani, N., Ghamri-Doudane, Y.: Anomaly-based framework for detecting power overloading cyberattacks in smart grid AMI. Comput. Secur. 96, 101,896 (2020)

    Article  Google Scholar 

  96. Kosek, A.M.: Contextual anomaly detection for cyber-physical security in smart grids based on an artificial neural network model. In: 2016 Joint Workshop on Cyber-Physical Security and Resilience in Smart Grids (CPSR-SG), IEEE, pp. 1–6 (2016)

  97. Kosek, A.M., Gehrke, O.: Ensemble regression model-based anomaly detection for cyber-physical intrusion detection in smart grids. In: 2016 Electrical Power and Energy Conference (EPEC), IEEE, pp. 1–7 (2016)

  98. Kumar, P., Lin, Y., Bai, G., Paverd, A., Dong, J.S., Martin, A.: Smart grid metering networks: a survey on security, privacy and open research issues. IEEE Commun. Surv. Tutor. 21(3), 2886–2927 (2019)

    Article  Google Scholar 

  99. Kurt, M.N., Ogundijo, O., Li, C., Wang, X.: Online cyber-attack detection in smart grid: a reinforcement learning approach. IEEE Trans. Smart Grid 10(5), 5174–5185 (2018)

    Article  Google Scholar 

  100. Kwon, Y., Kim, H.K., Lim, Y.H., Lim, J.I.: A behavior-based intrusion detection technique for smart grid infrastructure. In: 2015 Eindhoven PowerTech, IEEE, pp. 1–6 (2015)

  101. Kwon, Y., Lee, S., King, R., Lim, J.I., Kim, H.K.: Behavior analysis and anomaly detection for a digital substation on cyber-physical system. Electronics 8(3), 326 (2019)

    Article  Google Scholar 

  102. Li, M., Zhang, K., Liu, J., Gong, H., Zhang, Z.: Blockchain-based anomaly detection of electricity consumption in smart grids. Pattern Recognit. Lett. 138, 476–482 (2020)

    Article  Google Scholar 

  103. Li, R., Bhattacharjee, S., Das, S.K., Yamana, H.: Look-up table based FHE system for privacy preserving anomaly detection in smart grids. In: 2022 International Conference on Smart Computing (SMARTCOMP), IEEE, pp. 108–115 (2022)

  104. Li, S., Pandey, A., Hooi, B., Faloutsos, C., Pileggi, L.: Dynamic graph-based anomaly detection in the electrical grid. IEEE Trans. Power Syst. 37(5), 3408–3422 (2021)

    Article  Google Scholar 

  105. Linda, O., Manic, M., Vollmer, T.: Improving cyber-security of smart grid systems via anomaly detection and linguistic domain knowledge. In: 2012 5th International Symposium on Resilient Control Systems, IEEE, pp. 48–54 (2012)

  106. Lipčák, P., Macak, M., Rossi, B.: Big data platform for smart grids power consumption anomaly detection. In: 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), IEEE, pp. 771–780 (2019)

  107. Liu, C.C., Stefanov, A., Hong, J., Panciatici, P.: Intruders in the grid. IEEE Power Energy Mag. 10(1), 58–66 (2011)

    Article  Google Scholar 

  108. Liu, X., Nielsen, P.S.: Regression-based online anomaly detection for smart grid data. (2016) arXiv preprint arXiv:1606.05781

  109. Louk, M.H.L., Tama, B.A.: Revisiting gradient boosting-based approaches for learning imbalanced data: a case of anomaly detection on power grids. Big Data Cognit. Comput. 6(2), 41 (2022)

    Article  Google Scholar 

  110. Macola, I.G.: The five worst cyberattacks against the power industry since 2014. (2020) https://www.power-technology.com/features/the-five-worst-cyberattacks-against-the-power-industry-since2014/. Accessed 01 March 2023

  111. Madabhushi, S., Dewri, R.: Detection of demand manipulation attacks on a power grid. In: 2021 18th Annual International Conference on Privacy (PST), pp. 1–10. Security and Trust, IEEE (2021)

  112. Madabhushi, S., Dewri, R.: On the impact of model tolerance in power grid anomaly detection systems. In: 2022 18th International Conference on Information Systems Security (ICISS), Springer Nature Switzerland, pp. 220–234 (2022)

  113. Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. In: 2015 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp. 89–94 (2015)

  114. Mao, W., Cao, X., Yan, T., Zhang, Y.: Anomaly detection for power consumption data based on isolated forest. In: 2018 International Conference on Power System Technology (POWERCON), IEEE, pp. 4169–4174 (2018)

  115. Marino, D.L., Wickramasinghe, C.S., Amarasinghe, K., Challa, H., Richardson, P., Jillepalli, A.A., Johnson, B.K., Rieger, C., Manic, M.: Cyber and physical anomaly detection in smart-grids. In: 2019 Resilience Week (RWS), IEEE, vol 1, pp. 187–193 (2019)

  116. Marino, D.L., Wickramasinghe, C.S., Rieger, C., Manic, M.: Data-driven stochastic anomaly detection on smart-grid communications using mixture poisson distributions. In: 2019 45th Annual Conference of the IEEE Industrial Electronics Society, IEEE, pp. 5855–5861 (2019)

  117. Marnerides, A.K., Smith, P., Schaeffer-Filho, A., Mauthe, A.: Power consumption profiling using energy time-frequency distributions in smart grids. IEEE Commun. Lett. 19(1), 46–49 (2014)

    Article  Google Scholar 

  118. Mashima, D., Cárdenas, A.A.: Evaluating electricity theft detectors in smart grid networks. In: International Workshop on Recent Advances in Intrusion Detection, Springer, pp. 210–229 (2012)

  119. Mathur, A., Tippenhauer, N.O.: SWaT: A water treatment testbed for research and training on ics security. In: 2016 International Workshop on Cyber-physical Systems for Smart Water Networks (CySWater), pp. 31–36 (2016)

  120. Matthews, S.J., Leger, A.S.: Leveraging mapreduce and synchrophasors for real-time anomaly detection in the smart grid. IEEE Trans. Emerg. Top. Comput. 7(3), 392–403 (2017)

    Article  Google Scholar 

  121. Matthews, S.J., Leger, A.S.: Leveraging single board computers for anomaly detection in the smart grid. In: 2017 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), IEEE, pp. 437–443 (2017)

  122. Menon, D.M., Radhika, N.: Anomaly detection in smart grid traffic data for home area network. In: 2016 International Conference on Circuit, pp. 1–4. Power and Computing Technologies (ICCPCT), IEEE (2016)

  123. Moghaddass, R., Wang, J.: A hierarchical framework for smart grid anomaly detection using large-scale smart meter data. IEEE Trans. Smart Grid 9(6), 5820–5830 (2017)

    Article  Google Scholar 

  124. Mohammadi Rouzbahani, H., Karimipour, H., Rahimnejad, A., Dehghantanha, A., Srivastava, G.: Anomaly detection in cyber-physical systems using machine learning. In: Handbook of Big Data Privacy, Springer, pp. 219–235 (2020)

  125. Mookiah, L., Dean, C., Eberle, W.: Graph-based anomaly detection on smart grid data. In: 2017 30th International FLAIRS Conference, pp. 306–311 (2017)

  126. Moslemi, R., Davoodi, M., Velni, J.M.: A distributed approach for estimation of information matrix in smart grids and its application for anomaly detection. In: 2020 International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, pp. 1–7 (2020)

  127. Nam, H.S., Jeong, Y.K., Park, J.W.: An anomaly detection scheme based on LSTM autoencoder for energy management. In: 2020 International Conference on Information and Communication Technology Convergence (ICTC), IEEE, pp. 1445–1447 (2020)

  128. Nasr, P.M., Varjani, A.Y.: Alarm based anomaly detection of insider attacks in SCADA system. In: 2014 Smart Grid Conference (SGC), IEEE, pp. 1–6 (2014)

  129. National Centers for Environmental Information. Billion-dollar weather and climate disasters: Events. (2021) https://www.ncdc.noaa.gov/billions/events. Accessed 01 March 2023

  130. Neverman, A.: When the power grid fails—12 things you need to prepare (2022). https://commonsensehome.com/when-the-power-grid-fails/#Why_Does_The_Grid_Go_Down. Accessed 01 March 2023

  131. Nguyen, V.Q., Van Ma, L., Kim, J.y., Kim, K., Kim, J.: Applications of anomaly detection using deep learning on time series data. In: 2018 16th International Conference on Dependable, Autonomic and Secure Computing, 16th International Conference on Pervasive Intelligence and Computing, 4th International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), IEEE, pp. 393–396 (2018)

  132. Niu, X., Li, J., Sun, J., Tomsovic, K.: Dynamic detection of false data injection attack in smart grid using deep learning. In: 2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (ISGT), IEEE, pp. 1–6 (2019)

  133. Noureen, S.S., Bayne, S.B., Shaffer, E., Porschet, D., Berman, M.: Anomaly detection in cyber-physical system using logistic regression analysis. In: 2019 IEEE Texas Power and Energy Conference (TPEC), IEEE, pp. 1–6 (2019)

  134. Olivares-Rojas, J.C., Reyes-Archundia, E., Gutierrez-Gnecchi, J.A., Molina-Moreno, I., Cerda-Jacobo, J., Méndez-Patiño, A.: Towards cybersecurity of the smart grid using digital twins. IEEE Internet Comput. 26(3), 52–57 (2021)

    Article  Google Scholar 

  135. OpenEI. Phasor data concentrator (pdc). (2012) https://openei.org/wiki/Definition:Phasor_Data_Concentrator_(PDC). Accessed 01 March 2023

  136. Oprea, S.V., Bâra, A., Puican, F.C., Radu, I.C.: Anomaly detection with machine learning algorithms and big data in electricity consumption. Sustainability 13(19), 10,963 (2021)

    Article  Google Scholar 

  137. Otoum, S., Kantarci, B., Mouftah, H.T.: Mitigating false negative intruder decisions in WSN-based smart grid monitoring. In: 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), IEEE, pp. 153–158 (2017)

  138. Ouyang, Z., Sun, X., Yue, D.: Hierarchical time series feature extraction for power consumption anomaly detection. In: Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration, Springer, pp. 267–275 (2017)

  139. Ouyang, Z., Sun, X., Chen, J., Yue, D., Zhang, T.: Multi-view stacking ensemble for power consumption anomaly detection in the context of industrial internet of things. IEEE Access 6, 9623–9631 (2018)

    Article  Google Scholar 

  140. Pagliery, J.: Hackers attacked the U.S. energy grid 79 times this year. (2014) https://money.cnn.com/2014/11/18/technology/security/energy-grid-hack/. Accessed 01 March 2023

  141. Pan, K., Palensky, P., Esfahani, P.M.: From static to dynamic anomaly detection with application to power system cyber security. IEEE Trans. Power Syst. 35(2), 1584–1596 (2019)

    Article  Google Scholar 

  142. Panthi, M.: Anomaly detection in smart grids using machine learning techniques. In: 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T), IEEE, pp. 220–222 (2020)

  143. Parsai, S., Mahajan, S.: Anomaly Detection Using Long Short-Term Memory. In: 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), IEEE, pp. 333–337 (2020)

  144. Parthasarathy, S., Kundur, D.: Bloom filter based intrusion detection for smart grid SCADA. In: 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–6 (2012)

  145. Passerini, F., Tonello, A.M.: Smart grid monitoring using power line modems: anomaly detection and localization. IEEE Trans. Smart Grid 10(6), 6178–6186 (2019)

    Article  Google Scholar 

  146. Pereira, J., Silveira, M.: Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, pp. 1275–1282 (2018)

  147. Pliatsios, D., Sarigiannidis, P., Liatifis, T., Rompolos, K., Siniosoglou, I.: A novel and interactive industrial control system honeypot for critical smart grid infrastructure. In: 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), IEEE, pp. 1–6 (2019)

  148. Pliatsios, D., Sarigiannidis, P., Lagkas, T., Sarigiannidis, A.G.: A survey on SCADA systems: secure protocols, incidents, threats and tactics. IEEE Commun. Surv. Tutor. 22(3), 1942–1976 (2020)

    Article  Google Scholar 

  149. Promper, C., Engel, D., Green, R.C.: Anomaly detection in smart grids with imbalanced data methods. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 1–8 (2017)

  150. Qi, R., Rasband, C., Zheng, J., Longoria, R.: Detecting cyber attacks in smart grids using semi-supervised anomaly detection and deep representation learning. Information 12(8), 328 (2021)

    Article  Google Scholar 

  151. Qiu, H., Tu, Y., Zhang, Y.: Anomaly detection for power consumption patterns in electricity early warning system. In: 2018 10th International Conference on Advanced Computational Intelligence (ICACI), IEEE, pp. 867–873 (2018)

  152. Qu, Z., Liu, H., Wang, Z., Xu, J., Zhang, P., Zeng, H.: A combined genetic optimization with AdaBoost ensemble model for anomaly detection in buildings electricity consumption. Energy Build. 248(111), 193 (2021)

    Google Scholar 

  153. Rahimi, A., Shahrestani, A., Ramezani, S., Zamani, P., Tehrani, S.O., Moghaddam, M.H.Y.: Filter based time-series anomaly detection in AMI using AI approaches. In: 2021 5th International Conference on Internet of Things and Applications (IoT), IEEE, pp. 1–6 (2021)

  154. Raman, G., Peng, J.C.H., Rahwan, T.: Manipulating residents’ behavior to attack the urban power distribution system. IEEE Trans. Ind. Inf. 15(10), 5575–5587 (2019)

    Article  Google Scholar 

  155. Raman, G., AlShebli, B., Waniek, M., Rahwan, T., Peng, J.C.H.: How weaponizing disinformation can bring down a city’s power grid. PLoS ONE 15(8), 1–14 (2020)

    Article  Google Scholar 

  156. Rashid, H., Singh, P.: Monitor: An abnormality detection approach in buildings energy consumption. In: 2018 4th International Conference on Collaboration and Internet Computing (CIC), IEEE, pp. 16–25 (2018)

  157. Rashid, H., Arjunan, P., Singh, P., Singh, A.: Collect, compare, and score: a generic data-driven anomaly detection method for buildings. In: 2016 7th International Conference on Future Energy Systems Poster Sessions, pp. 1–2 (2016)

  158. Rashid, H., Stankovic, V., Stankovic, L., Singh, P.: Evaluation of non-intrusive load monitoring algorithms for appliance-level anomaly detection. In: 2019 International Conference on Acoustics, pp. 8325–8329. Speech and Signal Processing (ICASSP), IEEE (2019)

  159. Ravikumar, G., Govindarasu, M.: Anomaly detection and mitigation for wide-area damping control using machine learning. IEEE Trans. Smart Grid (2020)

  160. Rawat, D.B., Bajracharya, C.: Detection of false data injection attacks in smart grid communication systems. IEEE Signal Process. Lett. 22(10), 1652–1656 (2015)

    Article  Google Scholar 

  161. Rawat, S.S., Polavarapu, V.A., Kumar, V., Aruna, E., Sumathi, V.: Anomaly detection in smart grid using rough set theory and K cross validation. In: 2014 International Conference on Circuits, pp. 479–483. Power and Computing Technologies (ICCPCT), IEEE (2014)

  162. Ren, W., Yardley, T., Nahrstedt, K.: Edmand: Edge-based multi-level anomaly detection for SCADA networks. In: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, pp. 1–7 (2018)

  163. Reuter, L., Jung, O., Magin, J.: Neural network based anomaly detection for SCADA systems. In: 2020 23rd Conference on Innovation in Clouds, pp. 194–201. Internet and Networks and Workshops (ICIN), IEEE (2020)

  164. Rid, T.: Cyber war will not take place. J. Strateg. Stud. 35(1), 5–32 (2012)

    Article  Google Scholar 

  165. Rossi, B., Chren, S., Buhnova, B., Pitner, T.: Anomaly detection in smart grid data: an experience report. In: 2016 International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp. 002313–002318 (2016)

  166. Ruben, C., Dhulipala, S., Nagaraj, K., Zou, S., Starke, A., Bretas, A., Zare, A., McNair, J.: Hybrid data-driven physics model-based framework for enhanced cyber-physical smart grid security. IET Smart Grid 3(4), 445–453 (2020)

    Article  Google Scholar 

  167. Rubin, F.P., de Souza, P.S.S., dos Santos Marques, W., de Oliveira, R.R., Rossi, F.D., Ferreto, T.: Evaluating energy and thermal efficiency of anomaly detection algorithms in edge devices. In: 2020 International Conference on Information Networking (ICOIN), IEEE, pp. 208–213 (2020)

  168. Rösch, D., Ruhe, S., Schäfer, K., Nicolai, S.: Local anomaly detection analysis in distribution grid based on IEC 61850-9-2 LE SV voltage signals. In: 2019 International Conference on Smart Energy Systems and Technologies (SEST), IEEE, pp. 1–6 (2019)

  169. Saad, A., Sisworahardjo, N.: Data analytics-based anomaly detection in smart distribution network. In: 2017 International Conference on High Voltage Engineering and Power Systems (ICHVEPS), IEEE, pp. 1–5 (2017)

  170. Sahani, N., Zhu, R., Cho, J.H., Liu, C.C.: Machine learning-based intrusion detection for smart grid computing: a survey. ACM Transactions on Cyber-Physical Systems (2023)

  171. Sahu, N.K., Mukherjee, I.: Machine learning based anomaly detection for IoT network. In: 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI), IEEE, pp. 787–794 (2020)

  172. Sakhnini, J., Karimipour, H., Dehghantanha, A., Parizi, R.M., Srivastava, G.: Security aspects of internet of things aided smart grids: a bibliometric survey. Internet Things 14(100), 111 (2021)

    Google Scholar 

  173. Saraswat, D., Bhattacharya, P., Zuhair, M., Verma, A., Kumar, A.: Ansmart: A SVM-based anomaly detection scheme via system profiling in smart grids. In: 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), IEEE, pp. 417–422 (2021)

  174. Serrano-Guerrero, X., Escrivá-Escrivá, G., Luna-Romero, S., Clairand, J.M.: A time-series treatment method to obtain electrical consumption patterns for anomalies detection improvement in electrical consumption profiles. Energies 13(5), 1046 (2020)

    Article  Google Scholar 

  175. Shabad, P.K.R., Alrashide, A., Mohammed, O.: Anomaly detection in smart grids using machine learning. In: 2021 47th Annual Conference of the IEEE Industrial Electronics Society (IECON), IEEE, pp. 1–8 (2021)

  176. Shin, D.H., Zhang, J.: Early anomaly detection in an interconnected power grid and communication network: Exploiting interdependent structure of failures. In: 2015 IEEE Global Communications Conference (GLOBECOM), IEEE, pp. 1–6 (2015)

  177. Shouyu, L., Zhang, K., Fang, W., Zhou, Z., Hu, R., Zhu, W., Li, Y., Wang, Y., Hou, J.: Anomaly detection of power grid dispatching platform based on isolation forest and K-means fusion algorithm. J. Phys: Conf. Ser. 1601(2), 022,010 (2020)

    Google Scholar 

  178. Shylendra, A., Shukla, P., Mukhopadhyay, S., Bhunia, S., Trivedi, A.R.: Low power unsupervised anomaly detection by nonparametric modeling of sensor statistics. IEEE Trans. Very Large Scale Integr. VLSI Syst. 28(8), 1833–1843 (2020)

    Article  Google Scholar 

  179. Singh, S., Bhardwaj, S., Pandey, H., Beniwal, G.: Anomaly detection using federated learning. In: 2021 International Conference on Artificial Intelligence and Applications, Springer, pp. 141–148 (2021)

  180. Singh, V.K., Govindarasu, M.: Decision tree based anomaly detection for remedial action scheme in smart grid using pmu data. In: 2018 IEEE Power and Energy Society General Meeting (PESGM), IEEE, pp. 1–5 (2018)

  181. Singh, V.K., Govindarasu, M.: A cyber-physical anomaly detection for wide-area protection using machine learning. IEEE Trans. Smart Grid 12(4), 3514–3526 (2021)

    Article  Google Scholar 

  182. Singh, V.K., Ozen, A., Govindarasu, M.: A hierarchical multi-agent based anomaly detection for wide-area protection in smart grid. In: 2018 Resilience Week (RWS), IEEE, pp. 63–69 (2018)

  183. Siniosoglou, I., Efstathopoulos, G., Pliatsios, D., Moscholios, I.D., Sarigiannidis, A., Sakellari, G., Loukas, G., Sarigiannidis, P.: Neuralpot: an industrial honeypot implementation based on deep neural networks. In: 2020 IEEE Symposium on Computers and Communications (ISCC), IEEE, pp. 1–7 (2020)

  184. Siniosoglou, I., Radoglou-Grammatikis, P., Efstathopoulos, G., Fouliras, P., Sarigiannidis, P.: A unified deep learning anomaly detection and classification approach for smart grid environments. IEEE Trans. Netw. Serv. Manage. 18(2), 1137–1151 (2021)

    Article  Google Scholar 

  185. Sisworahardjo, N., Saad, A.A.: Spatio-temporal context anomaly detection for residential power consumption. Int. J. Electr. Eng. Inform. 9(4), 776–785 (2017)

    Google Scholar 

  186. Skopik, F., Friedberg, I., Fiedler, R.: Dealing with advanced persistent threats in smart grid ICT networks. In: 2014 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (ISGT), IEEE, pp. 1–5 (2014)

  187. Soltan, S., Mittal, P., Poor, H.V.: BlackIoT: IoT botnet of high wattage devices can disrupt the power grid. In: 2018 27th USENIX Security Symposium, pp. 15–32 (2018)

  188. Starke, A., McNair, J., Trevizan, R., Bretas, A., Peeples, J., Zare, A.: Toward resilient smart grid communications using distributed SDN with ML-based anomaly detection. In: 2018 International Conference on Wired/Wireless Internet Communication, Springer, pp. 83–94 (2018)

  189. Steiger, M., Bernard, J., Mittelstädt, S., Lücke-Tieke, H., Keim, D., May, T., Kohlhammer, J.: Visual analysis of time-series similarities for anomaly detection in sensor networks. Comput. Graph. Forum 33(3), 401–410 (2014)

    Article  Google Scholar 

  190. Storm, J.M., Hagen, J., Toftegaard, ØA.A.: A survey of using process data and features of industrial control systems in intrusion detection. In: 2021 IEEE International Conference on Big Data (Big Data), IEEE, pp. 2170–2177 (2021)

  191. Sun, M., Zhang, J.: Data-driven anomaly detection in modern power systems. In: Srikantha, P., Farag, H., Wei-Kocsis, J., Karimipour H. (eds.). Security of Cyber-Physical Systems, Springer, pp. 131–143 (2020)

  192. Takiddin, A., Ismail, M., Zafar, U., Serpedin, E.: Deep autoencoder-based anomaly detection of electricity theft cyberattacks in smart grids. IEEE Syst. J. (2022)

  193. Ten, C.W., Hong, J., Liu, C.C.: Anomaly detection for cybersecurity of the substations. IEEE Trans. Smart Grid 2(4), 865–873 (2011)

    Article  Google Scholar 

  194. Theumer, P., Zeiser, R., Trauner, L., Reinhart, G.: Anomaly detection on industrial time series for retaining energy efficiency. Procedia CIRP 99, 33–38 (2021)

    Article  Google Scholar 

  195. Thompson, D., Wang, H.: Integrated power signature generation circuit for iot abnormality detection. ACM J. Emerg. Technol. Comput. Syst. 18(1):1–13 (2021)

  196. Toshpulatov, M., Zincir-Heywood, N.: Anomaly detection on smart meters using hierarchical self organizing maps. In: 2021 Canadian Conference on Electrical and Computer Engineering (CCECE), IEEE, pp. 1–6 (2021)

  197. Tsukada, M., Kondo, M., Matsutani, H.: A neural network-based on-device learning anomaly detector for edge devices. IEEE Trans. Comput. 69(7), 1027–1044 (2020)

    MATH  Google Scholar 

  198. US Government Accountability Office. Electricity grid cybersecurity (2021). https://www.gao.gov/products/gao-21-81. Accessed 01 March 2023

  199. Utomo, D., Hsiung, P.A.: Anomaly detection at the IoT edge using deep learning. In: 2019 International Conference on Consumer Electronics-Taiwan (ICCE-TW), IEEE, pp. 1–2 (2019)

  200. Valdes, A., Macwan, R., Backes, M.: Anomaly detection in electrical substation circuits via unsupervised machine learning. In: 2016 17th International Conference on Information Reuse and Integration (IRI), IEEE, pp. 500–505 (2016)

  201. Valenzuela, J., Wang, J., Bissinger, N.: Real-time intrusion detection in power system operations. IEEE Trans. Power Syst. 28(2), 1052–1062 (2012)

    Article  Google Scholar 

  202. Wan Yen, S., Morris, S., Ezra, M.A., Jun Huat, T.: Effect of smart meter data collection frequency in an early detection of shorter-duration voltage anomalies in smart grids. Int. J. Electr. Power Energy Syst. 109, 1–8 (2019)

    Article  Google Scholar 

  203. Wang, D., Wang, X., Zhang, Y., Jin, L.: Detection of power grid disturbances and cyber-attacks based on machine learning. J. Inf. Secur. Appl. 46, 42–52 (2019)

    Google Scholar 

  204. Wang, P., Govindarasu, M.: Cyber-physical anomaly detection for power grid with machine learning. In: Industrial Control Systems Security and Resiliency, Springer, pp. 31–49 (2019)

  205. Wang, P., Govindarasu, M.: Cyber-Physical Anomaly Detection for Power Grid with Machine Learning, pp. 31–49. Springer, Cham (2019)

    Google Scholar 

  206. Wang, P., Govindarasu, M.: Multi-agent based attack-resilient system integrity protection for smart grid. IEEE Trans. Smart Grid 11(4), 3447–3456 (2020)

    Article  Google Scholar 

  207. Wang, Q., Tai, W., Tang, Y., Ni, M.: Review of the false data injection attack against the cyber-physical power system. IET Cyber-Phys. Syst. Theory Appl. 4(2), 101–107 (2019-06)

  208. Wang, X., Ahn, S.H.: Real-time prediction and anomaly detection of electrical load in a residential community. Appl. Energy 259, 114,145 (2020)

    Article  Google Scholar 

  209. Wang, X., Yang, I., Ahn, S.H.: Sample efficient home power anomaly detection in real time using semi-supervised learning. IEEE Access 7, 139,712-139,725 (2019)

    Article  Google Scholar 

  210. Wang, X., Zhao, T., Liu, H., He, R.: Power consumption predicting and anomaly detection based on long short-term memory neural network. In: 2019 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), IEEE, pp. 487–491 (2019)

  211. Wang, Z., Fu, Y., Song, C., Zeng, P., Qiao, L.: Power system anomaly detection based on OCSVM optimized by improved particle swarm optimization. IEEE Access 7, 181,580-181,588 (2019)

    Article  Google Scholar 

  212. Wei, Q., Ma, R., Wang, Y., Chen, M., Sun, Y., Liu, M., Lin, X.: Glad: A method of microgrid anomaly detection based on esd in smart power grid. In: 2020 International Conference on Power, pp. 103–107. Intelligent Computing and Systems (ICPICS), IEEE (2020)

  213. Weiss, M., Weiss, M.: An assessment of threats to the American power grid. Energy Sustain. Soc. 9(18), 1–9 (2019)

    Google Scholar 

  214. Wikipedia. Electricity grid simple- North America.svg. (2008) https://commons.wikimedia.org/wiki/File:Electricity_grid_simple-_North_America.svg. Accessed 01 March 2023

  215. Wilson, D., Tang, Y., Yan, J., Lu, Z.: Deep learning-aided cyber-attack detection in power transmission systems. In: 2018 IEEE Power and Energy Society General Meeting (PESGM), IEEE, pp. 1–5 (2018)

  216. Wu, J., Xiong, J., Shil, P., Shi. Y.: Real time anomaly detection in wide area monitoring of smart grids. In: 2014 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), IEEE, pp. 197–204 (2014)

  217. Xiang, B., Liu, Z., Zhang, K.: Flagging implausible inspection reports of distribution transformers via anomaly detection. IEEE Access 8, 75,798-75,808 (2020)

    Article  Google Scholar 

  218. Xiang, Y., Wang, L., Liu, N.: Coordinated attacks on electric power systems in a cyber-physical environment. Electr. Power Syst. Res. 149, 156–168 (2017)

    Article  Google Scholar 

  219. Xiao, Yj., Xu, Wy., Jia, Zh., Ma, Zr., Dl, Qi.: NIPAD: a non-invasive power-based anomaly detection scheme for programmable logic controllers. Front. Inf. Technol. Electron. Eng. 18(4), 519–534 (2017)

    Article  Google Scholar 

  220. Xu, A., Jiang, Y., Cao, Y., Zhang, G., Ji, X., Xu, W.: ADDP: anomaly detection for DTU based on power consumption side-channel. In: 2019 3rd Conference on Energy Internet and Energy System Integration (EI2), IEEE, pp. 2659–2663 (2019)

  221. Xu, A., Wu, T., Zhang, Y., Hu, Z., Jiang, Y.: Graph-based time series edge anomaly detection in smart grid. In: 2021 7th International Conference on Big Data Security on Cloud (BigDataSecurity), International Conference on High Performance and Smart Computing, (HPSC) and International Conference on Intelligent Data and Security (IDS), IEEE, pp. 1–6 (2021)

  222. Xu, C., Chen, H.: A hybrid data mining approach for anomaly detection and evaluation in residential buildings energy data. Energy Buildings 215, 109,864 (2020)

    Article  Google Scholar 

  223. Xu, C., Wang, J., Zhang, J., Li, X.: Anomaly detection of power consumption in yarn spinning using transfer learning. Comput. Ind. Eng. 152, 107,015 (2021)

    Article  Google Scholar 

  224. Yan, Y., Qian, Y., Sharif, H., Tipper, D.: A survey on smart grid communication infrastructures: motivations, requirements and challenges. IEEE Commun. Surv. Tutor. 15(1), 5–20 (2012)

    Article  Google Scholar 

  225. Yang, L., Li, F.: Detecting false data injection in smart grid in-network aggregation. In: 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm), IEEE, pp. 408–413 (2013)

  226. Yang, Y., Littler, T., Sezer, S., McLaughlin, K., Wang, H.F.: Impact of cyber-security issues on smart grid. In: 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, IEEE, pp. 1–7 (2011)

  227. Yen, S.W., Morris, S., Ezra, M.A., Huat, T.J.: Effect of smart meter data collection frequency in an early detection of shorter-duration voltage anomalies in smart grids. Int. J.Electr. Power Energy Syst. 109, 1–8 (2019)

    Article  Google Scholar 

  228. Yijia, T., Hang, G.: Anomaly detection of power consumption based on waveform feature recognition. In: 2016 11th International Conference on Computer Science and Education (ICCSE), IEEE, pp. 587–591 (2016)

  229. Yip, S.C., Tan, W.N., Tan, C., Gan, M.T., Wong, K.: An anomaly detection framework for identifying energy theft and defective meters in smart grids. Int. J. Electr. Power Energy Syst. 101, 189–203 (2018)

    Article  Google Scholar 

  230. Yu, J., Cheng, H., Zhang, J., Li, Q., Wu, S., Zhong, W., Ye, J., Song, W., Ma, P.: CONGO\(^2\): scalable online anomaly detection and localization in power electronics networks. IEEE Internet Things J. 9(15), 13,862-13,875 (2022)

    Article  Google Scholar 

  231. Yuan, Y., Jia, K.: A distributed anomaly detection method of operation energy consumption using smart meter data. In: 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), IEEE, pp. 310–313 (2015)

  232. Yïlmaz, Y., Uludag, S.: Timely detection and mitigation of IoT-based cyberattacks in the smart grid. J. Frankl. Inst. 358(1), 172–192 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  233. Zhang, J.E., Wu, D., Boulet, B.: Time series anomaly detection for smart grids: a survey. In: 2021 IEEE Electrical Power and Energy Conference (EPEC), IEEE, pp. 125–130 (2021)

  234. Zhang, L., Lv, Z., Zhang, X., Chen, C., Li, N., Li, Y., Wang, W.: A novel approach for traffic anomaly detection in power distributed control system and substation system. In: 2019 International Conference on Network and System Security, Springer, pp. 408–417 (2019)

  235. Zhang, L., Shen, X., Zhang, F., Ren, M., Ge, B., Li, B.: Anomaly detection for power grid based on time series model. In: 2019 International Conference on Computational Science and Engineering (CSE) and International Conference on Embedded and Ubiquitous Computing (EUC), IEEE, pp. 188–192 (2019)

  236. Zhang, L., Wan, L., Xiao, Y., Li, S., Zhu, C.: Anomaly detection method of smart meters data based on GMM-LDA clustering feature learning and PSO support vector machine. In: 2019 Sustainable Power and Energy Conference (ISPEC), IEEE, pp. 2407–2412 (2019)

  237. Zhang, Q., Wan, S., Wang, B., Gao, D.W., Ma, H.: Anomaly detection based on random matrix theory for industrial power systems. J. Syst. Architect. 95, 67–74 (2019)

    Article  Google Scholar 

  238. Zhang, Y., Chen, W., Black, J.: Anomaly detection in premise energy consumption data. In: 2011 Power and Energy Society General Meeting, IEEE, pp. 1–8 (2011)

  239. Zhao, H., Liu, H., Hu, W., Yan, X.: Anomaly detection and fault analysis of wind turbine components based on deep learning network. Renew. Energy 127, 825–834 (2018)

    Article  Google Scholar 

  240. Zhao, J., Wang, J., Yin, L.: Detection and control against replay attacks in smart grid. In: 2016 12th International Conference on Computational Intelligence and Security (CIS), IEEE, pp. 624–627 (2016)

  241. Zhou, F., Wen, G., Ma, Y., Geng, H., Huang, R., Pei, L., Yu, W., Chu, L., Qiu, R.: A comprehensive survey for deep-learning-based abnormality detection in smart grids with multimodal image data. Appl. Sci. 12(11), 5336 (2022)

    Article  Google Scholar 

  242. Zhou, M., Musilek, P.: Real-time anomaly detection in distribution grids using long short term memory network. In: 2021 IEEE Electrical Power and Energy Conference (EPEC), IEEE, pp. 208–213 (2021)

  243. Zhou, M., Wang, Y., Srivastava, A.K., Wu, Y., Banerjee, P.: Ensemble-based algorithm for synchrophasor data anomaly detection. IEEE Trans. Smart Grid 10(3), 2979–2988 (2018)

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Denver, Denver, CO, USA

    Srinidhi Madabhushi & Rinku Dewri

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  1. Srinidhi Madabhushi
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Srinidhi Madabhushi performed the collection and systematization of the literature, and wrote the main manuscript. Rinku Dewri supervised the collection and systematization process, and performed editorial modifications on the manuscript.

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Correspondence to Rinku Dewri.

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Appendix

Appendix

See Tables 3 and 4.

Table 3 Breakdown of references in literature by code
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Table 4 Breakdown of references in literature by detection technique
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Madabhushi, S., Dewri, R. A survey of anomaly detection methods for power grids. Int. J. Inf. Secur. 22, 1799–1832 (2023). https://doi.org/10.1007/s10207-023-00720-z

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