Abstract
With the advent of the era of big data, the application of Machine Learning (ML) is widely applied to the abnormal traffic detection. Detecting network anomalies plays an important role in network security. However, the large-scale traffic data detection is still a difficult problem at present. In this paper, we design a new algorithm that we called hinge classification algorithm based on mini-batch gradient descent (HCA-BAGD) to detect network anomalies. Compared with traditional traffic classification methods, such as Neural Network, Decision Tree, Logistic Regression, the algorithm can significantly boost the scale and speed of deep network training. We also solve the problem of data skew in Shuffle phase which has plagued the industry for a long time.
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References
Kruegel, C., Mutz, D., Robertson, W., Valeur, F.: Bayesian event classification for intrusion detection. In: 19th Annual Computer Security Applications Conference, Proceedings, pp. 14–23 (2003). doi:10.1109/CSAC.2003.1254306
Sinclair, C., Pierce, L., Matzner, S.: An application of machine learning to network intrusion detection. In: 15th Annual Computer Security Applications Conference, (ACSAC 1999), Proceedings, pp. 371–377 (1999). doi:10.1109/CSAC.1999.816048
Zhang, J., Zulkernine, M.: A hybrid network intrusion detection technique using random forests. In: The First International Conference on Availability, Reliability and Security, ARES 2006, p. 8 (2006). doi:10.1109/ARES.2006.7
Auld, T., Moore, A.W., Gull, S.F.: Bayesian neural networks for internet traffic classification. IEEE Trans. Neural Netw. 18(1), 223–239 (2007)
Williams, N., Zander, S., Armitage, G.: A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. SIGCOMM Comput. Commun. Rev. 36, 5–16 (2006)
Ohsaki, M., Wang, P., Matsuda, K., Katagiri, S., Watanabe, H., Ralescu, A.: Confusion-matrix-based Kernel logistic regression for imbalanced data classification. IEEE Trans. Knowl. Data Eng. 29, 1806–1819 (2017)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS (2014)
Ponulak, F., Kasinski, A.: Supervised learning in spiking neural networks with ReSuMe: sequence learning, classification, and spike shifting. Neural Comput. 22, 467–510 (2010)
Zhang, J., Xiang, Y., Wang, Y., Zhou, W., Xiang, Y., Guan, Y.: Network traffic classification using correlation information. IEEE Trans. Parallel Distrib. Syst. 24(1), 104–117 (2012). doi:10.1109/TPDS.2012.98
Biggio, B., Nelson, B., Laskov, P.: Support vector machines under adversarial label noise. Mach. Learn. 20(3), 97–112 (2011)
Graves, A.: Generating sequences with recurrent neural networks. In: Arxiv preprint arXiv:1308.0850 (2013)
Graves, A., Fernandez, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labeling unsegmented sequence data with recurrent neural networks. In: ICML (2006)
Tong, D., Qu, Y.R., Prasanna, V.K.: Accelerating decision tree based traffic classification on FPGA and multicore Platforms. IEEE Trans. Parallel Distrib. Syst. (2017). doi:10.1109/TPDS.2017.2714661
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This work was supported by National Natural Science Foundation of China (No. U1536122).
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Yan, X., Zhang, T., Cui, B., Deng, J. (2018). Hinge Classification Algorithm Based on Asynchronous Gradient Descent. In: Barolli, L., Xhafa, F., Conesa, J. (eds) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-69811-3_42
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DOI: https://doi.org/10.1007/978-3-319-69811-3_42
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