Abstract
With the explosive growth of the Internet, massive high-dimensional data and multiple attack types make intrusion detection systems face greater challenges. In practical application scenarios, the amount of abnormal data is small, and intrusion detection systems in different scenarios cannot be quickly migrated, and specific intrusion detection systems need to be trained for different scenarios, which greatly wastes manpower and material resources. Therefore, in view of the hierarchical characteristics of network data streams, this paper uses CNN and RNN networks to extract the spatiotemporal features of network data streams, then input them into GAN for unsupervised learning. Considering that long and short-term recurrent neural network (LSTM-RNN) has been shown to be able to obtain information and learn complex time series by remembering the backward (or even forward) time steps of cells, this paper replaces the generator and discriminator of GAN with LSTM-RNN. Anomaly detection is then performed based on residual loss and identification loss. Finally, this paper uses the deep domain adaptation algorithm to map the target domain and the source domain, and then optimizes the confusion loss of the domain by adversarial training, and finally extracts the invariant features of the target and the source domain.
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References
Lunt, T.F.: A survey of intrusion detection techniques. Comput. Secur. 12(4), 405–418 (1993)
Shibahara, T., Yagi, T., Akiyama, M., et al.: Efficient dynamic malware analysis based on network behavior using deep learning. In: 2016 IEEE Global Communications Conference (GLOBECOM), pp. 1–7. IEEE (2016)
David, O.E., Netanyahu, N.S.: DeepSign: deep learning for automatic malware signature generation and classification. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2015)
Pan, Y., Lin, J.: Malicious network stream generation and verification based on generative adversarial networks. J. East China Univ. Sci. Technol. 45(2), 165–171 (2019)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Discov. Data Eng. 22(10), 1345–1359 (2010)
Fengmei, W., Jianpei, Z., Yan, C., et al.: FSFP: transfer learning from long texts to the short. Appl. Math. Inf. Sci. 8(4), 2033 (2014)
Dai, W., Yang, Q., Xue, G.R., et al.: Self-taught clustering. In: Proceedings of the 25th International Conference on Machine Learning, pp. 200–207 (2008)
Samanta, S., Selvan, A.T., Das, S.: Cross-domain clustering performed by transfer of knowledge across domains. In: 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4. IEEE (2013)
Dai, W., Xue, G.R., Yang, Q., et al.: Co-clustering based classification for out-of-domain documents. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 210–219 (2007)
Dai, W.Y., Yang, Q., Xue, G.R., et al.: Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 193–200. Morgan Kaufmann Publishers, San Francisco (2007)
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Networks 22(2), 199–210 (2011). https://doi.org/10.1109/TNN.2010.2091281
Busto, P.P., Gall, J.: Open set domain adaptation. In: Proceedings of 2017 IEEE International Conference on Computer Vision, pp. 754–763. IEEE, Venice (2017)
Xue, Y., Xu, T., Zhang, H., et al.: SegAN: adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics 16(3), 383–392 (2018)
Yeh, R.A., Chen, C., Yian Lim, T., et al.: Semantic image inpainting with deep generative models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5485–5493 (2017)
Salimans, T., Goodfellow, I., Zaremba, W., et al.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146–157. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_12
Zenati, H., Foo, C.S., Lecouat, B., et al.: Efficient GAN-based anomaly detection. arXiv preprint arXiv:1802.06222 (2018)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Doriguzzi-Corin, R., Millar, S., Scott-Hayward, S., et al.: LUCID: a practical, lightweight deep learning solution for DDoS attack detection. IEEE Trans. Netw. Serv. Manage. 17(2), 876–889 (2020)
Yao, Y., Su, L., Lu, Z., et al.: STDeepGraph: spatial-temporal deep learning on communication graphs for long-term network attack detection. In: 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), pp. 120–127. IEEE (2019)
Abdulhammed, R., Musafer, H., Alessa, A., et al.: Features dimensionality reduction approaches for machine learning based network intrusion detection. Electronics 8(3), 322 (2019)
Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D., Li, W.: Deep reconstruction-classification networks for unsupervised domain adaptation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 597–613. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_36
Niu, J., Zhang, Y., Liu, D., et al.: Abnormal network traffic detection based on transfer component analysis. In: 2019 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–6. IEEE (2019)
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This work was supported by the Key R&D Program of Jiangsu (BE2022081).
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Fei, J., Sun, Y., Wang, Y., Lian, Z. (2024). Intrusion Detection System Based on Adversarial Domain Adaptation Algorithm. In: Jin, H., Yu, Z., Yu, C., Zhou, X., Lu, Z., Song, X. (eds) Green, Pervasive, and Cloud Computing. GPC 2023. Lecture Notes in Computer Science, vol 14503. Springer, Singapore. https://doi.org/10.1007/978-981-99-9893-7_17
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