Dongmei Bin
ABSTRACT
Power systems have revealed serious security problems in the process of gradual opening, and intrusion detection as an important security defense measure can detect potential intrusions in a timely manner. In the big data environment of electric power, there are information silos between different electric power data owners, and in order to obtain intrusion detection models with better performance, traditional methods need to fuse data from all parties, which often brings difficulties in information security and data privacy protection. In this paper, we propose a distributed intrusion detection framework based on federated learning and apply it to network traffic data analysis. The framework aims to ensure the information security of each local power data while establishing a collection of decentralized data and completing the joint training of models from multiple data sources. The experimental results show that the scheme achieves 98.1% accuracy on the simulated data set, which is better than other commonly used intrusion detection algorithms. In addition, the method well ensures the security and privacy of data because the data are not interoperable among each participant under the federated learning mechanism.
- Andresini, Giuseppina, Annalisa Appice and Donato Malerba, 2021. Nearest cluster-based intrusion detection through convolutional neural networks. Knowledge-Based Systems 216:106798 doi:https://doi.org/10.1016/j.knosys.2021.106798.Google Scholar
Cross Ref
- Yufei Liu and Dechang Pi. 2017. A Novel Kernel SVM Algorithm with Game Theory for Network Intrusion Detection. KSII Transactions on Internet and Information Systems, 11, 8, (2017), 4043-4060. DOI: 10.3837/tiis.2017.08.016.Google ScholarCross Ref
- Siniosoglou, Ilias, Panagiotis Radoglou-Grammatikis, Georgios Efstathopoulos, Panagiotis Fouliras and Panagiotis Sarigiannidis, 2021. A Unified Deep Learning Anomaly Detection and Classification Approach for Smart Grid Environments. IEEE Transactions on Network and Service Management 18(2):1137-1151 doi:10.1109/TNSM.2021.3078381.Google ScholarCross Ref
- Mendonça, Robson V., Arthur A. M. Teodoro, Renata L. Rosa, Muhammad Saadi, Dick Carrillo Melgarejo, Pedro H. J. Nardelli and Demóstenes Z. Rodríguez, 2021. Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network. IEEE Access 9:61024-61034 doi:10.1109/ACCESS.2021.3074664.Google ScholarCross Ref
- Wu, Kehe, Zuge Chen and Wei Li, 2018. A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks. IEEE Access 6:50850-50859 doi:10.1109/ACCESS.2018.2868993.Google ScholarCross Ref
- Liu, Yi, Neeraj Kumar, Zehui Xiong, Wei Yang Bryan Lim, Jiawen Kang & Dusit Niyato, 2020. Communication-Efficient Federated Learning for Anomaly Detection in Industrial Internet of Things.Google Scholar
- Abdel-Basset, Mohamed, Nour Moustafa, Hossam Hawash, Imran Razzak, Karam M. Sallam and Osama M. Elkomy, 2022. Federated Intrusion Detection in Blockchain-Based Smart Transportation Systems. IEEE Transactions on Intelligent Transportation Systems 23(3):2523-2537 doi:10.1109/TITS.2021.3119968.Google ScholarCross Ref
- Murat, Kuzlu, Pipattanasomporn Manisa and Rahman Saifur, 2014. Communication network requirements for major smart grid applications in HAN, NAN and WAN. Computer Networks 67:74-88 doi:https://doi.org/10.1016/j.comnet.2014.03.029.Google ScholarCross Ref
- Simonyan, Karen and Andrew Zisserman, 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv e-prints:arXiv:1409.1556.Google Scholar
- Brendan McMahan, H., Eider Moore, Daniel Ramage, Seth Hampson and Blaise Agüera y Arcas, 2016. Communication-Efficient Learning of Deep Networks from Decentralized Data. arXiv e-prints:arXiv:1602.05629.Google Scholar
- Tavallaee, Mahbod, Ebrahim Bagheri, Wei Lu and Ali A. Ghorbani, 2009. A detailed analysis of the KDD CUP 99 data set.Google Scholar
- Han, Hui, Wen-Yuan Wang and Bing-Huan Mao, Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning. In: Huang, D.-S., X.-P. Zhang & G.-B. Huang (eds) Advances in Intelligent Computing, Berlin, Heidelberg, 2005// 2005. Springer Berlin Heidelberg, p 878-887.Google Scholar
- Singh, Praneet, Jishnu Jaykumar P, Akhil Pankaj and Reshmi Mitra, 2021. Edge-Detect: Edge-Centric Network Intrusion Detection using Deep Neural Network.Google Scholar
Index Terms
- Federated Learning-Based Intrusion Detection Method for Smart Grid
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