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Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security

Maoran Xiao, Qi Zhou, Zhen Zhang, Junjie Yin

Abstract


Deep learning technologies have revolutionized the management of energy, energy consumption, and data security within smart grids through non-intrusive load monitoring (NILM). This paper explores the use of deep learning for real-time intrusion detection in power grids with a primary focus on safeguarding the integrity and security of Data Processing Units (DPUs). An evaluation of various machine learning models, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Trees, and Random Forests, is conducted to detect various types of intrusions, including Fault, Injection, Masquerade, Normal, and Replay. Random Forest produced AUC values of 1.00 for all classes and an overall F1-score of 0.99 for all classes. The Decision Tree model also shows robust performance for detecting Fault and Injection intrusions (AUC = 0.98), with an overall F1-score of 0.94. However, the LDA and SVM models do not perform well in detecting Injection intrusions with overall F1-scores of 0.83 and 0.86. Advances in machine learning can be used to improve smart grid security, reliability, and efficiency, according to this study. These findings highlight the potential of advanced machine learning techniques to enhance smart grid reliability and efficiency.

 

Doi: 10.28991/HIJ-2024-05-03-018

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Keywords


Machine Learning; Intrusion Detection; Smart Grids; Data Integrity; Security; NILM; Real-Time Detection; Energy Management.

References


Lu, L., Yu, D., Lin, P., Gu, C., Feng, J., & Yang, S. (2022). Non-intrusive Load Monitoring Method Based on BIC Event Detection and LSTM Network Model. 2022 3rd International Conference on Advanced Electrical and Energy Systems, AEES 2022, 238–242. doi:10.1109/AEES56284.2022.10079312.

Adewole, K. S., & Torra, V. (2024). Energy disaggregation risk resilience through micro-aggregation and discrete Fourier transform. Information Sciences, 662. doi:10.1016/j.ins.2024.120211.

Dowalla, K., Bilski, P., Łukaszewski, R., Wójcik, A., & Kowalik, R. (2022). Application of the Time-Domain Signal Analysis for Electrical Appliances Identification in the Non-Intrusive Load Monitoring. Energies, 15(9), 3325. doi:10.3390/en15093325.

Tan, X., Li, W., Xu, X., Ao, G., Zhou, F., Zhao, J., Tan, Q., & Zhang, W. (2022). Contactless AC/DC Wide-Bandwidth Current Sensor Based on Composite Measurement Principle. Sensors, 22(20), 7979. doi:10.3390/s22207979.

Zhao, K., Zhang, R., Zhang, Y., Cai, Q., & Shu, J. (2021). An Event-Detection Algorithm for Non-intrusive Load Monitoring of Residential Appliances. Lecture Notes in Electrical Engineering, 718, 781–800. doi:10.1007/978-981-15-9746-6_59.

Ma, L., Meng, Q., Pan, S., & Liebman, A. (2021). PUMPNET: a deep learning approach to pump operation detection. Energy Informatics, 4(1), 1-17. doi:10.1186/s42162-020-00135-3.

Han, Y., Xu, Y., Huo, Y., & Zhao, Q. (2021). Non-intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing. IET Generation, Transmission and Distribution, 15(21), 3066–3080. doi:10.1049/gtd2.12242.

Zhang, G., Ji, X., Li, Y., & Xu, W. (2020). Power-based non-intrusive condition monitoring for terminal device in smart grid. Sensors (Switzerland), 20(13), 1–17. doi:10.3390/s20133635.

Bucci, G., Ciancetta, F., Fiorucci, E., Mari, S., & Fioravanti, A. (2021). State of art overview of Non-Intrusive Load Monitoring applications in smart grids. Measurement: Sensors, 18. doi:10.1016/j.measen.2021.100145.

Silva, M. D., & Liu, Q. (2024). A Review of NILM Applications with Machine Learning Approaches. Computers, Materials and Continua, 79(2), 2971–2989. doi:10.32604/cmc.2024.051289.

Adewole, K. S., & Torra, V. (2022). Privacy Issues in Smart Grid Data: From Energy Disaggregation to Disclosure Risk. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13426 LNCS, 71–84. doi:10.1007/978-3-031-12423-5_6.

Kommey, B., Tamakloe, E., Kponyo, J. J., Tchao, E. T., Agbemenu, A. S., & Nunoo-Mensah, H. (2024). An artificial intelligence-based non-intrusive load monitoring of energy consumption in an electrical energy system using a modified K-Nearest Neighbour algorithm. IET Smart Cities, 134-155. doi:10.1049/smc2.12075.

Tsai, M. S., & Lin, Y. K. (2023). Applying the Geometric Features of Cumulative Sums to the Development of Event Detection. Energies, 16(20), 7207. doi:10.3390/en16207207.

Liu, Y., Qiu, J., & Ma, J. (2022). SAMNet: Toward Latency-Free Non-Intrusive Load Monitoring via Multi-Task Deep Learning. IEEE Transactions on Smart Grid, 13(3), 2412–2424. doi:10.1109/TSG.2021.3139395.

Seyedi, Y., Karimi, H., Wetté, C., & Sansó, B. (2020). A New Approach to Reliability Assessment and Improvement of Synchrophasor Communications in Smart Grids. IEEE Transactions on Smart Grid, 11(5), 4415–4426. doi:10.1109/TSG.2020.2993944.

Green, D. H., Shaw, S. R., Lindahl, P., Kane, T. J., Donnal, J. S., & Leeb, S. B. (2020). A MultiScale Framework for Nonintrusive Load Identification. IEEE Transactions on Industrial Informatics, 16(2), 992–1002. doi:10.1109/TII.2019.2923236.

Xia, M., Liu, W., Wang, K., Song, W., Chen, C., & Li, Y. (2020). Non-intrusive load disaggregation based on composite deep long short-term memory network. Expert Systems with Applications, 160. doi:10.1016/j.eswa.2020.113669.

Pereira, L. (2019). NILMPEds: A performance evaluation dataset for event detection algorithms in non-intrusive load monitoring. Data, 4(3), 127. doi:10.3390/data4030127.

Huang, Q. (2018). Review: Energy-efficient smart building driven by emerging sensing, communication, and machine learning technologies. Engineering Letters, 26(3), 320–332.

Henao, N., Agbossou, K., Kelouwani, S., Hosseini, S. S., & Fournier, M. (2018). Power estimation of multiple two-state loads using a probabilistic non-intrusive approach. Energies, 11(1), 88. doi:10.3390/en11010088.

Otoum, S., Kantarci, B., & Mouftah, H. T. (2017). Detection of Known and Unknown Intrusive Sensor Behavior in Critical Applications. IEEE Sensors Letters, 1(5), 1-4. doi:10.1109/LSENS.2017.2752719.

Villani, C., Benatti, S., Brunelli, D., & Benini, L. (2017). A contactless, energy-neutral power meter for smart city applications. Lecture Notes in Electrical Engineering, 429, 177–182. doi:10.1007/978-3-319-55071-8_23.

Eibl, G., & Engel, D. (2015). Influence of data granularity on smart meter privacy. IEEE Transactions on Smart Grid, 6(2), 930–939. doi:10.1109/TSG.2014.2376613.

Chan, A. C. F., & Zhou, J. (2023). Non-Intrusive Protection for Legacy SCADA Systems. IEEE Communications Magazine, 61(6), 36–42. doi:10.1109/MCOM.003.2200564.

Liu, H., Fu, Y., Pan, K., Xu, W., Li, C., & Liu, C. (2023). Attack Detection for Distributed Photovoltaic Generation Systems Leveraging Cyber and Power Side Channel Data. 2023 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2023, 1-5. doi:10.1109/ISGTAsia54891.2023.10372698.

Etezadifar, M., Karimi, H., Aghdam, A. G., & Mahseredjian, J. (2023). Resilient Event Detection Algorithm for Non-Intrusive Load Monitoring Under Non-Ideal Conditions Using Reinforcement Learning. IEEE Transactions on Industry Applications, 60(2), 2085–2094. doi:10.1109/TIA.2023.3307347.

Lin, Y. H. (2022). An advanced smart home energy management system considering identification of ADLs based on non-intrusive load monitoring. Electrical Engineering, 104(5), 3391–3409. doi:10.1007/s00202-022-01546-z.


Full Text: PDF

DOI: 10.28991/HIJ-2024-05-03-018

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