Abstract
Existing supervised learning methods are difficult to adapt the rapidly evolving network attacks. They are effective for malicious flows with clear features, but struggle with flows that reveal unclear or sparse characteristics. This is a concern as malicious flows are rare and discrete in real-world situations. To overcome these challenges, this research paper introduces a novel few-shot sample malicious flow detection model that leverages data augmentation techniques. The model’s core objective is to train agents to distinguish between normal and malicious flows. On this basis, the model enhances the agents’ ability to recognize malicious flows through discrete information interactions. Experimental results confirm that the data augmentation method effectively improves the agents’ understanding of network traffic. Additionally, it successfully enhances intrusion detection capabilities in multiple agents, diverse datasets, and varied scenarios. Notably, in few-shot sample scenarios, the method greatly boosts the overall accuracy rate.
This work is financially supported by the National Natural Science Foundation of China under Grant 62106060.
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References
Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., Ahmad, F.: Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans. Emerg. Telecommun. Technol. 32(1), e4150 (2021)
Albini, E., Long, J., Dervovic, D., Magazzeni, D.: Counterfactual shapley additive explanations. In: 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 1054–1070 (2022)
Arora, S., Ge, R., Liang, Y., Ma, T., Zhang, Y.: Generalization and equilibrium in generative adversarial nets (GANs). In: International Conference on Machine Learning, pp. 224–232. PMLR (2017)
Arulkumaran, K., Deisenroth, M.P., Brundage, M., Bharath, A.A.: Deep reinforcement learning: a brief survey. IEEE Signal Process. Mag. 34(6), 26–38 (2017)
Ghosh, A., Kulharia, V., Namboodiri, V.P., Torr, P.H., Dokania, P.K.: Multi-agent diverse generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8513–8521 (2018)
Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)
Gronauer, S., Diepold, K.: Multi-agent deep reinforcement learning: a survey. Artif. Intell. Rev., 1–49 (2022)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Jais, I.K.M., Ismail, A.R., Nisa, S.Q.: Adam optimization algorithm for wide and deep neural network. Knowl. Eng. Data Sci. 2(1), 41–46 (2019)
Liu, M., Chang, W., Li, C., Ji, Y., Li, R., Feng, M.: Discrete interactions in decentralized multiagent coordination: a probabilistic perspective. IEEE Trans. Cogn. Dev. Syst. 13(4), 1010–1022 (2021)
Liu, M., et al.: Modeling and analysis of the decentralized interactive cyber defense approach. China Commun. 19(10), 116–128 (2022)
Niu, W., Zhou, J., Zhao, Y., Zhang, X., Peng, Y., Huang, C.: Uncovering apt malware traffic using deep learning combined with time sequence and association analysis. Comput. Secur. 120, 102809 (2022)
Palli, A.S., Jaafar, J., Hashmani, M.A., Gomes, H.M., Gilal, A.R.: A hybrid sampling approach for imbalanced binary and multi-class data using clustering analysis. IEEE Access 10, 118639–118653 (2022)
Zhang, J., Wang, T., Ng, W.W., Zhang, S., Nugent, C.D.: Undersampling near decision boundary for imbalance problems. In: 2019 International Conference on Machine Learning And Cybernetics (ICMLC), pp. 1–8. IEEE (2019)
Zhang, Z., Zeng, Y., Bai, L., Hu, Y., Wu, M., Wang, S., Hancock, E.R.: Spectral bounding: strictly satisfying the 1-Lipschitz property for generative adversarial networks. Pattern Recogn. 105, 107179 (2020)
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Liu, M., Jia, Y., Li, C., Fu, P., Zhang, Z. (2024). Multi-agent Cooperative Intrusion Detection Based on Generative Data Augmentation. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14492. Springer, Singapore. https://doi.org/10.1007/978-981-97-0811-6_19
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DOI: https://doi.org/10.1007/978-981-97-0811-6_19
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