Abstract
The network intrusion detection system is an essential guarantee for network security. Most research on network intrusion detection systems focuses on using supervised learning algorithms, which require a large amount of labeled data for training. However, the work of labeling data is complex and cannot exhaustively include all types of network intrusion. Therefore, in this study, we develop a model that only requires normal data in the training phase, and it can distinguish between normal data and abnormal data in the test phase. This model is implemented by using a generative confrontation network. Experimental results show that, on the CIC-IDS-2017 dataset, our model has an accuracy of 97%, which is dramatically higher than the basic autoencoder, which is one of the most widely used algorithms in the network intrusion detection.
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Acknowledgment
Partially Funded by Science and Technology Program of Sichuan Province (2021YFG0330), partially funded by Grant SCITLAB-0001 of Intelligent Terminal Key La-boratory of SiChuan Province,and partially Funded by Fundamental Research Funds for the Central Universities (ZYGX2019J076).
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Li, X., Lin, D., Tang, Y., Wu, W., Li, Z., Chen, B. (2022). Using Generative Adversarial Networks for Network Intrusion Detection. In: Shi, S., Ma, R., Lu, W. (eds) 6GN for Future Wireless Networks. 6GN 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 439. Springer, Cham. https://doi.org/10.1007/978-3-031-04245-4_6
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DOI: https://doi.org/10.1007/978-3-031-04245-4_6
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