Abstract
Data is a valuable strategic resource for the development of modern society. However, with the increasingly complex network environment, privacy leaks and malicious attacks emerge in endlessly. For example, blockchain has also begun to become a new outlet for network black production, which poses a huge security threat to cryptocurrency. In this paper, we propose a hybrid network model (Cb Net), which uses Convolutional Neural Networks (CNN) and Bidirectional recurrent neural networks (BiGRU) to fully extract the space-time characteristics of network data traffic. Then, we propose an intrusion detection method (FLD), which introduces federated learning to collect traffic data from different network institutions, analyze network traffic and identify network attacks. We have fully evaluated the performance of the proposed model and method on the public dataset NSL-KDD. Experiments show that the proposed hybrid network model can achieve high detection accuracy, and the FLD method can effectively identify network attacks on the premise of ensuring the privacy of the local data of the users involved, and its performance is better than other methods.
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Acknowledgment
This work was supported by Hainan Provincial Natural Science Foundation of China(Grant No. 620MS021, 621QN211), the Key Research and Development Program of Hainan Province (Grant No. ZDYF2021GXJS003, ZDYF2020040), the Major science and technology project of Hainan Province (Grant No. ZDKJ2020012), National Natural Science Foundation of China (NSFC) (Grant No. 62162022, 62162024), Science and Technology Development Center of the Ministry of Education Industry-university-Research Innovation Fund (2021JQR017), Innovative research project for Graduate students in Hainan Province (Grant No.Qhyb2022-93).
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Kou, Y., Cheng, J., Yang, Y., Wu, H., Li, Y., Sheng, V.S. (2024). Network Intrusion Detection Based on Hybrid Network Model and Federated Learning. In: Qiu, X., Xiao, Y., Wu, Z., Zhang, Y., Tian, Y., Liu, B. (eds) The 7th International Conference on Information Science, Communication and Computing. ISCC2023 2023. Smart Innovation, Systems and Technologies, vol 350. Springer, Singapore. https://doi.org/10.1007/978-981-99-7161-9_12
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DOI: https://doi.org/10.1007/978-981-99-7161-9_12
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