Abstract
With the widespread deployment of 5G networks, there is a massive increase in data volume and types of 5G network traffic. This results in the emergence of severely imbalanced data distribution, including imbalances between benign and malicious traffic, as well as imbalances among different types of malicious traffic. This imbalanced data distribution presents significant challenges for intrusion detection systems (IDSs). Existing IDSs in 5G networks often treat diverse traffic classes equally, overlooking the varying costs of misclassification across classes. This leads to lower accuracy for minority traffic classes. Additionally, researchers use various sampling methods to balance benign and malicious traffic in 5G networks but often ignore imbalances among different types of malicious traffic, hindering specific identification improvements. Moreover, the redundant features employed by current IDSs contribute to suboptimal detection performance across varied scenarios in 5G networks. In this paper, we present CoSen-IDS, an innovative intrusion detection system designed for 5G networks based on cost-sensitive learning to solve imbalanced issues. Specifically, CoSen-IDS dynamically identifies the crucial features within 5G network traffic by evaluating feature importance. Moreover, we propose a cost-sensitive strategy that guides Generative Adversarial Networks to amplify multiple minority traffic classes simultaneously in the training dataset. Finally, we aggregate different base classifiers to attain a more robust intrusion detection model. Through extensive experiments on two datasets, our results show that CoSen-IDS outperforms relevant state-of-the-art methods.
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References
Imanbayev, A., et al.: Research of machine learning algorithms for the development of intrusion detection systems in 5G mobile networks and beyond. Sensors 22, 9957 (2022). https://doi.org/10.3390/s22249957
Ahmad, I., Kumar, T., Liyanage, M., Okwuibe, J., Ylianttila, M., Gurtov, A.: Overview of 5G security challenges and solutions. IEEE Commun. Stand. Mag. 2, 36–43 (2018). https://doi.org/10.1109/MCOMSTD.2018.1700063
Li, X., Chen, W., Zhang, Q., Wu, L.: Building auto-encoder intrusion detection system based on random forest feature selection. Comput. Secur. 95, 101851 (2020). https://doi.org/10.1016/j.cose.2020.101851
Mirsky, Y., tshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection. arXiv preprint arXiv:1802.09089 (2018)
Zhang, H., Li, J.-L., Liu, X.-M., Dong, C.: Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detection. Futur. Gener. Comput. Syst. 122, 130–143 (2021). https://doi.org/10.1016/j.future.2021.03.024
Feng, F., Li, K.-C., Shen, J., Zhou, Q., Yang, X.: Using cost-sensitive learning and feature selection algorithms to improve the performance of imbalanced classification. IEEE Access 8, 69979–69996 (2020). https://doi.org/10.1109/ACCESS.2020.2987364
Sharafaldin, I., Habibi Lashkari, A., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: Proceedings of the 4th International Conference on Information Systems Security and Privacy, pp. 108–116. SCITEPRESS - Science and Technology Publications, Funchal, Madeira, Portugal (2018). https://doi.org/10.5220/0006639801080116
Basha, S.J., Madala, S.R., Vivek, K., Kumar, E.S., Ammannamma, T.: A review on imbalanced data classification techniques. In: 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), pp. 1–6. IEEE, Coimbatore (2022). https://doi.org/10.1109/ICACTA54488.2022.9753392
Chatzoglou, E., Goudos, S.K.: Beam-selection for 5g/b5g networks using machine learning: a comparative study. Sensors 23, 2967 (2023). https://doi.org/10.3390/s23062967
Mohanty, S.K., Subudhi, A., Sahoo, S.K.: A comparison of oversampling and transformation techniques used for analysis of PAPR in a real and complex OFDM system for 5G applications. In: Das, S., Mohanty, M.N. (eds.) Advances in Intelligent Computing and Communication. LNNS, vol. 202, pp. 275–286. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-0695-3_27
Andresini, G., Appice, A., De Rose, L., Malerba, D.: GAN augmentation to deal with imbalance in imaging-based intrusion detection. Futur. Gener. Comput. Syst. 123, 108–127 (2021)
Kumar, V.: Generative adversarial networks-aided intrusion detection system. In: Generative Adversarial Networks and Deep Learning, pp. 79–98. Chapman and Hall/CRC (2023)
Lashkari, A.H., Gil, G.D., Mamun, M.S.I., Ghorbani, A.A.: Characterization of tor traffic using time based features. In: ICISSP 2017 - Proceedings of the 3rd International Conference on Information Systems Security and Privacy, pp. 253–262. SciTePress (2017). https://doi.org/10.5220/0006105602530262
Strobl, C., Boulesteix, A.-L., Zeileis, A., Hothorn, T.: Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinform. 8, 25 (2007). https://doi.org/10.1186/1471-2105-8-25
Fisher, A., Rudin, C., Dominici, F.: All models are wrong, but many are useful: learning a variable’s importance by studying an entire class of prediction models simultaneously. J. Mach. Learn. Res. 20, 1–81 (2019)
Dare, P.: Linear and nonlinear models. Fixed effects, random effects, and mixed models. Geomatica 60, 382–383 (2006)
Linja, J., Hämäläinen, J., Nieminen, P., Kärkkäinen, T.: Feature selection for distance-based regression: An umbrella review and a one-shot wrapper. Neurocomputing 518, 344–359 (2023)
Ding, H., Chen, L., Dong, L., Fu, Z., Cui, X.: Imbalanced data classification: a KNN and generative adversarial networks-based hybrid approach for intrusion detection. Futur. Gener. Comput. Syst. 131, 240–254 (2022). https://doi.org/10.1016/j.future.2022.01.026
Ding, S., Kou, L., Wu, T.: A GAN-based intrusion detection model for 5G enabled future metaverse. Mobile Netw Appl. 27, 2596–2610 (2022). https://doi.org/10.1007/s11036-022-02075-6
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv:1411.1784v1 (2014)
Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems. Curran Associates, Inc. (2017)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Bhati, B.S., Rai, C.S.: Ensemble based approach for intrusion detection using extra tree classifier. In: Solanki, V.K., Hoang, M.K., Lu, Z.(, Pattnaik, P.K. (eds.) Intelligent Computing in Engineering. AISC, vol. 1125, pp. 213–220. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-2780-7_25
Chimphlee, W., Chimphlee, S.: Network intrusion detector using multilayer perceptron (MLP) approach (2022)
Samarakoon, S., et al.: 5G-NIDD: a comprehensive network intrusion detection dataset generated over 5G wireless network. arXiv:2212.01298 (2022)
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This work was supported by the National Key Research and Development Program of China (No. 2021YFB2910108).
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Yuan, L., Sun, J., Zhuang, S., Liu, Y., Geng, L., Ma, W. (2024). CoSen-IDS: A Novel Cost-Sensitive Intrusion Detection System on Imbalanced Data in 5G Networks. In: Huang, DS., Chen, W., Pan, Y. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14869. Springer, Singapore. https://doi.org/10.1007/978-981-97-5603-2_39
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