Abstract
This paper proposes a novel approach called KDSVM, which utilized the k-mean techniques and advantage of feature learning with deep neural network (DNN) model and strong classifier of support vector machines (SVM) , to detection intrusion networks. KDSVM is composed of two stages. In the first step, the dataset is divided into k subset based on every sample distance by the cluster centers of k-means approach, and in the second step, testing dataset is distanced by the same cluster center and fed into the DNN model with SVM model for intrusion detection. The experimental results show that the KDSVM not only performs better than SVM, BPNN, DBN-SVM (Salama et al., Soft computing in industrial applications, 2011 [21]) and Bayes tree models in terms of detection accuracy and abnormal types of attacks found. It also provides an effective tool for the study and analysis of intrusion detection in the large network.
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Aburomman, A.A., Reaz, M.B.I.: A novel svm-knn-pso ensemble method for intrusion detection system. Applied Soft Computing 38, 360–372 (2016)
Alom, M.Z., Bontupalli, V., Taha, T.M.: Intrusion detection using deep belief networks. In: 2015 National Aerospace and Electronics Conference (NAECON). pp. 339–344. IEEE (2015)
Barbara, D., Wu, N., Jajodia, S.: Detecting novel network intrusions using bayes estimators. In: SDM. pp. 1–17. SIAM (2011)
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. Neural Networks, IEEE Transactions on 5(2), 157–166 (1994)
Chen, W.H., Hsu, S.H., Shen, H.P.: Application of svm and ann for intrusion detection. Computers & Operations Research 32(10), 2617–2634 (2005)
Chilimbi, T., Suzue, Y., Apacible, J., Kalyanaraman, K.: Project adam: Building an efficient and scalable deep learning training system. In: 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14). pp. 571–582 (2014)
Denning, D.E.: An intrusion-detection model. Software Engineering, IEEE Transactions on SE-13(2), 222–232 (1987)
Dokas, P., Ertoz, L., Kumar, V., Lazarevic, A., Srivastava, J., Tan, P.N.: Data mining for network intrusion detection. In: Proc. NSF Workshop on Next Generation Data Mining. pp. 21–30 (2002)
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? The Journal of Machine Learning Research 11, 625–660 (2010)
Grover, A., Kapoor, A., Horvitz, E.: A deep hybrid model for weather forecasting. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 379–386. ACM (2015)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural computation 18(7), 1527–1554 (2006)
Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length, and helmholtz free energy. Advances in neural information processing systems pp. 3–3 (1994)
Huang, P.S., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using click through data. In: Proceedings of the 22nd ACM international Conference on information & knowledge management. pp. 2333–2338. ACM (2013)
Japkowicz, N., Shah, M.: Evaluating learning algorithms: a classification perspective. Cambridge University Press (2011)
Kabiri, P., Ghorbani, A.A.: Research on intrusion detection and response: A survey. IJ Network Security 1(2), 84–102 (2005)
Karami, A., Guerrero-Zapata, M.: A fuzzy anomaly detection system based on hybrid pso-kmeans algorithm in content-centric networks. Neurocomputing 149, 1253–1269 (2015)
Kayacik, H.G., Zincir-Heywood, A.N., Heywood, M.I.: A hierarchical som-based intrusion detection system. Engineering Applications of Artificial Intelligence 20(4), 439–451 (2007)
Koc, L., Mazzuchi, T.A., Sarkani, S.: A network intrusion detection system based on a hidden naive bayes multiclass classifier. Expert Systems with Applications 39(18), 13492–13500 (2012)
Marin, G.: Network security basics. Security & Privacy, IEEE 3(6), 68–72 (2005)
Palm, R.B.: Prediction as a candidate for learning deep hierarchical models of data. Technical University of Denmark (2012)
Salama, M.A., Eid, H.F., Ramadan, R.A., Darwish, A., Hassanien, A.E.: Hybrid intelligent intrusion detection scheme. In: Soft computing in industrial applications, pp. 293–303. Springer (2011)
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 Defence Applications 2009 (2009)
Zhang, J., Zulkernine, M., Haque, A.: Random-forests-based network intrusion detection systems. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 38(5), 649–659 (2008)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant No. 11361046) and the Key Research Fund of Ningxia Normal University (Grant No. NXSFZD1517 NXSFZD1603 and NXSFZD1608), the Natural Science Fund of Ningxia Province (Grant NZ16260) and the Fundamental Research Fund for Senior School of Ningxia Province (Grant No. NGY2015124).
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Ma, T., Yu, Y., Wang, F., Zhang, Q., Chen, X. (2018). A Hybrid Methodologies for Intrusion Detection Based Deep Neural Network with Support Vector Machine and Clustering Technique. In: Yen, N., Hung, J. (eds) Frontier Computing. FC 2016. Lecture Notes in Electrical Engineering, vol 422. Springer, Singapore. https://doi.org/10.1007/978-981-10-3187-8_13
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