Intrusion detection and mitigation of attacks in microgrid using enhanced deep belief network

References

• Adhikari, U., T. H. Morris, and S. Pan. 2018. Applying non-nested generalized exemplars classification for cyber-power event and intrusion detection. IEEE Transactions on Smart Grid 9 (5). doi: 10.1109/TSG.2016.2642787.
   PubMed Web of Science ®Google Scholar
• Asri, S., and B. Pranggono. 2015. Impact of distributed denial-of-service attack on advanced metering infrastructure. Wireless Personal Communications 83 (3):2211–23. doi:10.1007/s11277-015-2510-3.
   Web of Science ®Google Scholar
• Ayo, F. E., S. O. Folorunso, A. A. Abayomi-Alli, A. O. Adekunle, and J. B. Awotunde. 2020. Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection. Information Security Journal 29 (6). doi: 10.1080/19393555.2020.1767240.
   Web of Science ®Google Scholar
• Balakrishnan, N., A. Rajendran, D. Pelusi, and V. Ponnusamy. 2021. Deep belief network enhanced intrusion detection system to prevent security breach in the internet of things. Internet of Things 14:100112. doi:10.1016/j.iot.2019.100112.
   Google Scholar
• Canaan, B., B. Colicchio, and D. O. Abdeslam. 2020. Microgrid cyber-security: Review and challenges toward resilience. Applied Sciences (Switzerland) 10 (16). doi: 10.3390/app10165649.
   Google Scholar
• Chekired, D. A., L. Khoukhi, and H. T. Mouftah. 2019. Fog-based distributed intrusion detection system against false metering attacks in smart grid. IEEE International Conference on Communications, (Shanghai, China: Institute of Electrical and Electronics Engineers Inc.,), May. 10.1109/ICC.2019.8761752
   Google Scholar
• Denise Tellbach, J.-F. L. 2018. Cyber-attacks on smart meters in household nanogrid: modeling, simulation and analysis. Energies 11 (316). doi: 10.3390/en11020316.
   Google Scholar
• Ding, Y., S. Chen, and J. Xu (2016). Application of Deep Belief Networks for opcode based malware detection. Proceedings of the International Joint Conference on Neural Networks, Vancouver, BC, Canada, Octob. 10.1109/IJCNN.2016.7727705
   Google Scholar
• Diro, A. A., and N. Chilamkurti. 2018. Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems 82:761–68. doi:10.1016/j.future.2017.08.043.
   Web of Science ®Google Scholar
• Duan, J., W. Zeng, and M. Y. Chow. 2018. Resilient distributed DC optimal power flow against data integrity attack. IEEE Transactions on Smart Grid. 10.1109/TSG.2016.2633943
   Google Scholar
• Erfani, S. M., S. Rajasegarar, S. Karunasekera, and C. Leckie. 2016. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognition 58:121–34. doi:10.1016/j.patcog.2016.03.028.
   Web of Science ®Google Scholar
• Fu, R., X. Huang, J. Sun, Z. Zhou, D. Chen, and Y. Wu. 2017. Stability analysis of the cyber physical microgrid system under the intermittent DoS attacks. Energies 10 (5):680. doi:10.3390/en10050680.
   Web of Science ®Google Scholar
• Gallo, A. J., M. S. Turan, F. Boem, G. Ferrari-Trecate, and T. Parisini. 2018. Distributed watermarking for secure control of microgrids under replay attacks. IFAC-PapersOnLine 51 (23):182–87. doi:10.1016/j.ifacol.2018.12.032.
   Google Scholar
• Hinton, G. E., S. Osindero, and Y. W. Teh. 2006. A fast learning algorithm for deep belief nets. Neural Computation 18 (7):1527–54. doi:10.1162/neco.2006.18.7.1527.
   PubMed Web of Science ®Google Scholar
• Hu, B., C. Zhou, Y. C. Tian, Y. Qin, and X. Junping. 2019. A collaborative intrusion detection approach using blockchain for multimicrogrid systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems 49 (8). doi: 10.1109/TSMC.2019.2911548.
   Web of Science ®Google Scholar
• James Ranjith, K. R., D. K., and B. S. 2019. Transient model-based detection scheme for false data injection attacks in microgrids. 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Beijing, China, 1–6. 10.1109/SmartGridComm.2019.8909780
   Google Scholar
• Kavousi-Fard, A., W. Su, and T. Jin. 2021. A machine-learning-based cyber attack detection model for wireless sensor networks in microgrids. IEEE Transactions on Industrial Informatics 17 (1):650–58. doi:10.1109/TII.2020.2964704.
   Web of Science ®Google Scholar
• Kurt, M. N., O. Ogundijo, C. Li, and X. Wang. 2018. Online cyber-attack detection in smart grid: a reinforcement learning approach. IEEE Transactions on Smart Grid, 10(5): 5174–5185. 10.1109/TSG.2018.2878570
   Google Scholar
• Larochelle, H., and Y. Bengio. 2008. Classification using discriminative restricted Boltzmann machines. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland. 10.1145/1390156.1390224
   Google Scholar
• Liu, Q., V. Hagenmeyer, and H. B. Keller. 2021. A review of rule learning-based intrusion detection systems and their prospects in smart grids. In IEEE Access 9. doi:10.1109/ACCESS.2021.3071263.
   Google Scholar
• Liu, S., P. Siano, and X. Wang. 2020. Intrusion-detector-dependent frequency regulation for microgrids under denial-of-service attacks. IEEE Systems Journal 14 (2):2593–96. doi:10.1109/JSYST.2019.2935352.
   Web of Science ®Google Scholar
• Lu, N., Y. Sun, H. Liu, and S. Li. 2018. Intrusion detection system based on evolving rules for wireless sensor networks. Journal of Sensors 2018:1–8. doi:10.1155/2018/5948146.
   Web of Science ®Google Scholar
• Meng, W., W. Li, and L. F. Kwok. 2015. Design of intelligent KNN-based alarm filter using knowledge-based alert verification in intrusion detection. Security and Communication Networks 8 (18):3883–95. doi:10.1002/sec.1307.
   Web of Science ®Google Scholar
• Mitchell, R., and I. R. Chen. 2013. Behavior-rule based intrusion detection systems for safety critical smart grid applications. IEEE Transactions on Smart Grid 4 (3):1254–63. doi:10.1109/TSG.2013.2258948.
   Web of Science ®Google Scholar
• Industrial Control System (ICS) Cyber Attack Datasets. (n.d.). Retrieved December 31, 2020, from https://sites.google.com/a/uah.edu/tommy-morris-uah/ics-data-sets
   Google Scholar
• Naik, N., R. Diao, and Q. Shen. 2018. Dynamic fuzzy rule interpolation and its application to intrusion detection. IEEE Transactions on Fuzzy Systems 26 (4):1878–92. doi:10.1109/TFUZZ.2017.2755000.
   Web of Science ®Google Scholar
• Ojugo, A. A., A. O. Eboka, O. E. Okonta, R. E. Yoro, and F. O. Aghware. 2012. Genetic Algorithm Rule-Based Intrusion Detection System (GAIDS). Journal of Emerging Trends in Computing Information Systems, 3 (8): 1182- 1194.
   Google Scholar
• Otuoze, A. O., M. Wazir Mustafa, and R. M. L. 2018. Smart grids security challenges: Classification by sources of threats. Journal of Electrical Systems and Information Technology 5 (3):468–83. doi:10.1016/j.jesit.2018.01.001.
   Google Scholar
• Pavithra, L., and D. Rekha. 2021. Prevention of replay attack for isolated smart grid. Advances in Intelligent Systems and Computing. doi:10.1007/978-981-15-4851-2_27.
   Google Scholar
• Poudel, S., Z. Ni, X. Zhong, and H. He. 2016. Comparative studies of power grid security with network connectivity and power flow information using unsupervised learning. Proceedings of the International Joint Conference on Neural Networks, Vancouver, BC, Canada, 2016 Octob, 2730–37. 10.1109/IJCNN.2016.7727542
   Google Scholar
• Radoglou-Grammatikis, P. I., and P. G. Sarigiannidis. 2019. Securing the smart grid: A comprehensive compilation of intrusion detection and prevention systems. IEEE Access 7:46595–620. doi:10.1109/ACCESS.2019.2909807.
   Web of Science ®Google Scholar
• Rana, M. M., L. Li, and S. W. Su. 2018. Cyber attack protection and control of microgrids. IEEE/CAA Journal of Automatica Sinica 5 (2):602–09. doi:10.1109/JAS.2017.7510655.
   Google Scholar
• Rekik, M., Z. Chtourou, C. Gransart, and A. Atieh. 2018. A cyber-physical threat analysis for microgrids. 2018 15th International Multi-Conference on Systems, Signals and Devices, SSD, Yasmine Hammamet, Tunisia, 2018, 731–37. 10.1109/SSD.2018.8570411
   Google Scholar
• Sahoo, S., S. Mishra, J. C. H. Peng, and T. Dragicevic. 2019. A stealth cyber-attack detection strategy for DC microgrids. IEEE Transactions on Power Electronics 34 (8):8162–74. doi:10.1109/TPEL.2018.2879886.
   Web of Science ®Google Scholar
• Sangeetha, S., S. Haripriya, S. G. Mohana Priya, V. Vaidehi, and N. Srinivasan. 2010. Fuzzy rule-base based intrusion detection system on application layer. Communications in Computer and Information Science 89 CCIS. doi:10.1007/978-3-642-14478-3_3.
   Google Scholar
• Sharma, A., and M. L. Kolhe. 2021. Impacts of electricity pricing on techno-economic performance of photovoltaic-battery centered microgrid. Energy Sources, Part A: Recovery, Utilization and Environmental Effects 1–16. doi:10.1080/15567036.2021.1905112.
   Web of Science ®Google Scholar
• Shiva, P., N. Zhen, and N. M. 2017. Real-time cyber physical system testbed for power system security and control. International Journal of Electrical Power & Energy Systems 90:124–33. doi:10.1016/j.ijepes.2017.01.016.
   Web of Science ®Google Scholar
• Singh, S. K., K. Khanna, R. Bose, B. K. Panigrahi, and A. Joshi. 2018. Joint-transformation-based detection of false data injection attacks in smart grid. IEEE Transactions on Industrial Informatics 14 (1):89–97. doi:10.1109/TII.2017.2720726.
   Web of Science ®Google Scholar
• Tan, S., W. Z. Song, M. Stewart, J. Yang, and L. Tong. 2018. Online data integrity attacks against real-time electrical market in smart grid. IEEE Transactions on Smart Grid 9 (1):313–22. doi:10.1109/TSG.2016.2550801.
   Web of Science ®Google Scholar
• Tang, Z., Y. Lin, M. Vosoogh, N. Parsa, A. Baziar, B. Khan, Z. Tang, and B. Khan. 2021. Securing microgrid optimal energy management using deep generative model. IEEE Access 9. doi:10.1109/ACCESS.2021.3074460.
   Web of Science ®Google Scholar
• Tian, Q., D. Han, K. Li, X. Liu, et al. 2020. An intrusion detection approach based on improved deep belief network. Applied Intelligence. 50(10):3162–78. doi:10.1007/s10489-020-01694-4.
   Web of Science ®Google Scholar
• Turner, C., R. Jeremiah, D. Richards, and A. Joseph. 2016. A rule status monitoring algorithm for rule-based intrusion detection and prevention systems. Procedia Computer Science 95:361–68. doi:10.1016/j.procs.2016.09.346.
   Google Scholar
• Umar, S., and M. Felemban. 2021. Rule-based detection of false data injections attacks against optimal power flow in power systems. Sensors 21 (7):2478. doi:10.3390/s21072478.
   PubMed Web of Science ®Google Scholar
• Ustun, T. S., S. M. Suhail Hussain, A. Ulutas, A. Onen, M. M. Roomi, and D. Mashima. 2021. Machine learning-based intrusion detection for achieving cybersecurity in smart grids using IEC 61850 GOOSE messages. Symmetry 13 (5):826. doi:10.3390/sym13050826.
   Google Scholar
• Venkataramanan, V., A. Hahn, and A. Srivastava. 2019. Cyphyr: A cyber-physical analysis tool for measuring and enabling resiliency in microgrids. IET Cyber-Physical Systems: Theory and Applications 4 (4). doi: 10.1049/iet-cps.2018.5069.
   Google Scholar
• Wei, J., and J. M. Gihan. 2016. A deep learning-based cyber-physical strategy to mitigate false data injection attack in smart grids. 2016 Joint Workshop on Cyber- Physical Security and Resilience in Smart Grids, Vienna, Austria, 1–6. 10.1109/CPSRSG.2016.7684102
   Google Scholar
• Wei, L., D. G., and C. L. 2018. False data injection attacks detection with deep belief networks in smart grid. 2018 Chinese Automation Congress (CAC) 2621–25. doi:10.1109/CAC.2018.8623514.
   Google Scholar
• Xi, W., S. He, R. Chen, Y. Xu, W. Li, G. Zhou, W. Yu, H. He, Z. Huang, Y. Yu, et al. 2020. Research on attack detection method of microgrid central controller based on convolutional neural network. Journal of Physics. Conference Series 1646 (1). doi: 10.1088/1742-6596/1646/1/012076.
   Google Scholar
• Yang, Q., D. Li, W. Yu, Y. Liu, D. An, X. Yang, and J. Lin. 2017a. Toward data integrity attacks against optimal power flow in smart grid. IEEE Internet of Things Journal, 4(5), 1726–1738 . 10.1109/JIOT.2017.2709252
   Google Scholar
• Yang, X., P. Zhao, X. Zhang, J. Lin, and W. Yu. 2017b. Toward a gaussian-mixture model-based detection scheme against data integrity attacks in the smart grid. IEEE Internet of Things Journal. 10.1109/JIOT.2016.2631520
   Google Scholar
• Ye, Z., H. Yang, and M. Zheng. 2021. Using modified prediction interval-based machine learning model to mitigate data attack in microgrid. International Journal of Electrical Power and Energy Systems 129:106847. doi:10.1016/j.ijepes.2021.106847.
   Web of Science ®Google Scholar
• Yi, P., T. Zhu, Q. Zhang, Y. Wu, and L. Pan. 2016. Puppet attack: A denial of service attack in advanced metering infrastructure network. Journal of Network and Computer Applications 59:325–32. doi:10.1016/j.jnca.2015.04.015.
   Web of Science ®Google Scholar
• Yun, L., L. Yan, J.-W. Ren, and D. S. 2014. Research on efficient detection methods for false data injection in smart grid. 2014 International Conference on Wireless Communication and Sensor Network, Wuhan, China, 188–92. 10.1109/WCSN.2014.45
   Google Scholar
• Zhang, K., Z. Hu, Y. Zhan, X. Wang, and K. Guo. 2020. A smart grid AMI intrusion detection strategy based on extreme learning machine. Energies 13 (18). doi: 10.3390/en13184907.
   PubMed Web of Science ®Google Scholar
• Zhao, J., J. Wang, and L. Yin. 2017. Detection and control against replay attacks in smart grid. Proceedings - 12th International Conference on Computational Intelligence and Security, CIS 2016, Wuxi, China. 10.1109/CIS.2016.150
   Google Scholar
• Zou, L. 2021. Detection Method of DC microgrid network attack based on two-level and multi-segment model. Wireless Personal Communications. https://doi.org/10.1007/s11277-021-08711-w
   Web of Science ®Google Scholar