Abstract
Intrusion detection systems are becoming more necessary because the number of intrusion attacks on servers is increasing. Attackers try to intrude on the servers in various ways. Therefore, intrusion detection systems based on machine learning are required because it is hard to make the detection rules manually. This paper presents a deep neural network for intrusion detection systems. In addition, this paper shows experimental results which indicate the performance of the proposed neural network. The experimental results in this paper also indicate why detecting intrusion attacks is not easy.
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References
Al-Yaseen, W.L., Othman, Z.A., Nazri, M.Z.A.: Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system. Expert Syst. Appl. 67, 296–303 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Ilgun, K., Kemmerer, R.A., Porras, P.A.: State transition analysis: a rule-based intrusion detection approach. IEEE Trans. Softw. Eng. 21(3), 181–199 (1995)
Kim, D.S., Nguyen, H.N., Park, J.S.: Genetic algorithm to improve SVM based network intrusion detection system. In: Proceedings of the 19th International Conference on Advanced Information Networking and Applications (AINA2005), vol. 2, pp. 155–158. IEEE (2005)
Komar, M., Sachenko, A., Golovko, V., Dorosh, V.: Compression of network traffic parameters for detecting cyber attacks based on deep learning. In: Proceedings of the 9th International Conference on Dependable Systems, Services and Technologies (DESSERT), pp. 43–47. IEEE (2018)
Labonne, M., Olivereau, A., Polvé, B., Zeghlache, D.: A cascade-structured meta-specialists approach for neural network-based intrusion detection. In: Proceedings of the 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp. 1–6. IEEE (2019)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (CVPR), pp. 2980–2988 (2017)
Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: Proceedings of the 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1–6. IEEE (2015)
Resende, P.A.A., Drummond, A.C.: A survey of random forest based methods for intrusion detection systems. ACM Comput. Surv. (CSUR) 51(3), 1–36 (2018)
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: Proceedings of the Second IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1–6. IEEE (2009)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (NIPS2017), vol. 30 (2017)
Wu, P., Guo, H.: LuNET: a deep neural network for network intrusion detection. In: Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 617–624. IEEE (2019)
Wu, P., Guo, H., Moustafa, N.: Pelican: a deep residual network for network intrusion detection. In: Proceedings of the 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), pp. 55–62. IEEE (2020)
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This work is supported by JSPS KAKENHI Grant Number JP20K11871.
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Takeda, A. (2022). Detection and Analysis of Intrusion Attacks Using Deep Neural Networks. In: Barolli, L., Miwa, H., Enokido, T. (eds) Advances in Network-Based Information Systems. NBiS 2022. Lecture Notes in Networks and Systems, vol 526. Springer, Cham. https://doi.org/10.1007/978-3-031-14314-4_26
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DOI: https://doi.org/10.1007/978-3-031-14314-4_26
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