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Appl. Sci. 2018, 8(9), 1535;

A Filter Feature Selection Algorithm Based on Mutual Information for Intrusion Detection

National Engineering Laboratory for Disaster Backup and Recovery, Information Security Center, School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
Author to whom correspondence should be addressed.
Received: 26 July 2018 / Revised: 29 August 2018 / Accepted: 30 August 2018 / Published: 1 September 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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For a large number of network attacks, feature selection is used to improve intrusion detection efficiency. A new mutual information algorithm of the redundant penalty between features (RPFMI) algorithm with the ability to select optimal features is proposed in this paper. Three factors are considered in this new algorithm: the redundancy between features, the impact between selected features and classes and the relationship between candidate features and classes. An experiment is conducted using the proposed algorithm for intrusion detection on the KDD Cup 99 intrusion dataset and the Kyoto 2006+ dataset. Compared with other algorithms, the proposed algorithm has a much higher accuracy rate (i.e., 99.772%) on the DOS data and can achieve better performance on remote-to-login (R2L) data and user-to-root (U2R) data. For the Kyoto 2006+ dataset, the proposed algorithm possesses the highest accuracy rate (i.e., 97.749%) among the other algorithms. The experiment results demonstrate that the proposed algorithm is a highly effective feature selection method in the intrusion detection. View Full-Text
Keywords: feature selection; mutual information; intrusion detection; classification feature selection; mutual information; intrusion detection; classification

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Zhao, F.; Zhao, J.; Niu, X.; Luo, S.; Xin, Y. A Filter Feature Selection Algorithm Based on Mutual Information for Intrusion Detection. Appl. Sci. 2018, 8, 1535.

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