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2019 | OriginalPaper | Buchkapitel

Learning Automata Based SVM for Intrusion Detection

verfasst von : Chong Di, Yu Su, Zhuoran Han, Shenghong Li

Erschienen in: Communications, Signal Processing, and Systems

Verlag: Springer Singapore

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Abstract

As an indispensable defensive measure of network security, the intrusion detection is a process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents. It is a classifier to judge the event is normal or malicious. The information used for intrusion detection contains some redundant features which would increase the difficulty of training the classifier for intrusion detection and increase the time of making predictions. To simplify the training process and improve the efficiency of the classifier, it is necessary to remove these dispensable features. in this paper, we propose a novel LA-SVM scheme to automatically remove redundant features focusing on intrusion detection. This is the first application of learning automata for solving dimension reduction problems. The simulation results indicate that the LA-SVM scheme achieves a higher accuracy and is more efficient in making predictions compared with traditional SVM.

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Literatur
1.
Zurück zum Zitat Scarfone, K., Mell, P.: Guide to intrusion detection and prevention systems (IDPS). NIST Spec. Publ. 800(2007), 94 (2007) Scarfone, K., Mell, P.: Guide to intrusion detection and prevention systems (IDPS). NIST Spec. Publ. 800(2007), 94 (2007)
2.
Zurück zum Zitat Chen, W.H., Hsu, S.H., Shen, H.P.: Application of SVM and ANN for intrusion detection. Comput. Oper. Res. 32(10), 2617–2634 (2005)CrossRef Chen, W.H., Hsu, S.H., Shen, H.P.: Application of SVM and ANN for intrusion detection. Comput. Oper. Res. 32(10), 2617–2634 (2005)CrossRef
3.
Zurück zum Zitat Zhao, M., Chow, T.W.S., Wu, Z., Zhang, Z., Li, B.: Learning from normalized local and global discriminative information for semi-supervised regression and dimensionality reduction. Inf. Sci. 324, 286–309 (2015)CrossRef Zhao, M., Chow, T.W.S., Wu, Z., Zhang, Z., Li, B.: Learning from normalized local and global discriminative information for semi-supervised regression and dimensionality reduction. Inf. Sci. 324, 286–309 (2015)CrossRef
4.
Zurück zum Zitat Narendra, K.S., Thathachar, M.A.: Learning Automata: An Introduction. Courier Corporation, North Chelmsford (2012) Narendra, K.S., Thathachar, M.A.: Learning Automata: An Introduction. Courier Corporation, North Chelmsford (2012)
5.
Zurück zum Zitat Esnaashari, M., Meybodi, M.R.: Data aggregation in sensor networks using learning automata. Wireless Netw. 16(3), 687–699 (2010)CrossRef Esnaashari, M., Meybodi, M.R.: Data aggregation in sensor networks using learning automata. Wireless Netw. 16(3), 687–699 (2010)CrossRef
6.
Zurück zum Zitat Jiang, W., Zhao, C.L., Li, S.H., Chen, L.: A new learning automata based approach for online tracking of event patterns. Neurocomputing 137, 205–211 (2014)CrossRef Jiang, W., Zhao, C.L., Li, S.H., Chen, L.: A new learning automata based approach for online tracking of event patterns. Neurocomputing 137, 205–211 (2014)CrossRef
7.
Zurück zum Zitat Nicopolitidis, P., Papadimitriou, G.I., Pomportsis, A.S.: Using learning automata for adaptive push-based data broadcasting in asymmetric wireless environments. IEEE Trans. Veh. Technol. 51(6), 1652–1660 (2002)CrossRef Nicopolitidis, P., Papadimitriou, G.I., Pomportsis, A.S.: Using learning automata for adaptive push-based data broadcasting in asymmetric wireless environments. IEEE Trans. Veh. Technol. 51(6), 1652–1660 (2002)CrossRef
8.
Zurück zum Zitat Hernández-Pereira, E., Suárez-Romero, J.A., Fontenla-Romero, O., Alonso-Betanzos, A.: Conversion methods for symbolic features: a comparison applied to an intrusion detection problem. Expert Syst. Appl. 36(7), 10612–10617 (2009)CrossRef Hernández-Pereira, E., Suárez-Romero, J.A., Fontenla-Romero, O., Alonso-Betanzos, A.: Conversion methods for symbolic features: a comparison applied to an intrusion detection problem. Expert Syst. Appl. 36(7), 10612–10617 (2009)CrossRef
Metadaten
Titel
Learning Automata Based SVM for Intrusion Detection
verfasst von
Chong Di
Yu Su
Zhuoran Han
Shenghong Li
Copyright-Jahr
2019
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-6571-2_252

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