Abstract
Although being isolated from the external network, the private network is still faced with some security threats, such as violations communications, malware attacks, and illegal operations. It is an attractive approach to recognize these security threats by discovering the underlying anomalous traffic. By studying the anomalous traffic detection technologies, an anomalous traffic detection approach is developed by capturing and analyzing the network packets, detecting the anomaly traffic that occurs in the network, and then detects anomalous behaviors of the network timely. In order to enhance its effectiveness and efficiency, a self-learning model is proposed and deployed in the detection approach. Finally, we conduct necessary evaluations about the proposed approach. The test results show that the approach can reach a good effect for detecting the unknown anomalous traffic.
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References
Hasan, M.S., ElShakankiry, A., Dean, T., Zulkernine, M.: Intrusion detection in a private network by satisfying constraints. In: 2016 14th Annual Conference on Privacy, Security and Trust. Auckland, New Zealand, 12–14 December 2016. https://doi.org/10.1109/PST.2016.7906997
Han, W., Xue, J., Yan, H.: Detecting anomalous traffic in the controlled network based on cross entropy and support vector machine. IET Inf. Secur. 13(2), 109–116 (2019). https://doi.org/10.1049/iet-ifs.2018.5186
Vijayasarathy, R., Raghavan, S.V., Ravindran, B.: A system approach to network modeling for DDoS detection using a Naìve Bayesian classifier. In: Proceedings of 2011 Third International Conference on Communication Systems and Networks, Bangalore, India, 4–8 January 2011. https://doi.org/10.1109/COMSNETS.2011.5716474
Swarnkar, M., Hubballi, N.: OCPAD: one class Naive Bayes classifier for payload based anomaly detection. Expert Syst. Appl. 64, 330–339 (2016)
Li, W., Li, Q.X.: Using Naive Bayes with AdaBoost to enhance network anomaly intrusion detection. In: Proceedings of International Conference on Intelligent Networks & Intelligent Systems, pp. 486–489. IEEE Computer Society (2010). https://doi.org/10.1109/ICINIS.2010.133
Ahirwar, D.K., Saxena, S.K., Sisodia, M.S.: Anomaly detection by Naive Bayes & RBF network. Int. J. Adv. Res. Comput. Sci. Electron. Eng. 1(1), 14–18 (2012)
Peng, T., Tang, Z.: A small scale forecasting algorithm for network traffic based on relevant local least squares support vector machine regression model. Appl. Math. Inf. Sci. 9(2), 653–659 (2015). https://doi.org/10.12785/amis/092L41
Catania, C.A., Bromberg, F., Garino, C.G.: An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection. Expert Syst. Appl. 39(2), 1822–1829 (2010)
Ji, S.Y., Choi, S., Dong, H.J.: Designing a two-level monitoring method to detect network anomalous behaviors. In: Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, Redwood City, CA, USA, 13–15 August 2014. IEEE (2014). https://doi.org/10.1109/IRI.2014.7051958
Li, S., Yun, X., Zhang, Y.: A model of trojan communication behavior detection based on hierarchical clustering technique. Comput. Res. Dev. (s2), 9–16 (2012)
Yu, H., Wang, J.: Analysis of network traffic based on IP address clustering. J. Ocean Univ. China Nat. Sci. Ed. (s1), 196–199 (2008)
Wang, X., Liang, X.: Network traffic prediction model based on BPSO-RBFNN. Comput. Appl. Softw. (9), 102–105 (2014)
Ravale, U., Marathe, N., Padiya, P.: Feature selection based hybrid anomaly intrusion detection system using k means and RBF kernel function. Procedia Comput. Sci. 45(39), 428–435 (2015)
Lykov, S., Asakura, Y.: Anomalous traffic pattern detection in large urban areas: tensor-based approach with continuum modeling of traffic flow. Int. J. Intell. Transp. Syst. Res. 18(1), 13–21 (2018). https://doi.org/10.1007/s13177-018-0167-5
Hou, E., Yılmaz, Y., Hero, A.O.: Anomaly detection in partially observed traffic networks. IEEE Trans. Signal Process. 67(6), 1461–1476 (2019). https://doi.org/10.1109/TSP.2019.2892026
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This work was supported by the National Key Research and Development Program of China under Grant 2016QY06X1205.
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Han, W., Xue, J., Zhang, F., Zhang, Y. (2020). An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12486. Springer, Cham. https://doi.org/10.1007/978-3-030-62223-7_3
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DOI: https://doi.org/10.1007/978-3-030-62223-7_3
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