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Learning Automata Based SVM for Intrusion Detection

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)

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.

Keywords

Intrusion detection Network security Learning automata Dimension reduction 

Notes

Acknowledgements

This research work is funded by the State Grid Corporation of China (SGCC) Science and Technology Project (SGRIXTKJ [2017] 133), the National Key Research and Development Project of China (2016YFB0801003), and the Key Laboratory for Shanghai Integrated Information Security Management Technology Research.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Cyber SecurityShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Shanghai Key Laboratory of Integrated Administration Technologies for Information SecurityShanghaiChina

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