An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection

Authors:
Shih-Wei Lin;Kuo-Ching Ying;Chou-Yuan Lee;Zne-Jung Lee
Affiliations:
Department of Information Management, Chang Gung University, No. 259, Wen-Hwa 1st Road, Tao-Yuan, Taiwan;Department of Industrial Engineering and Management, National Taipei University of Technology No. 1, Sec. 3, Chung-hsiao E. Rd., Taipei 10608, Taiwan;Department of Information Management, Lan Yang Institute of Technology, Taiwan;Department of Information Management, Huafan University, No. 1, Huafan Rd., Shihding District, New Taipei County 22301, Taiwan
Venue:
Applied Soft Computing
Year:
2012

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Abstract

Intrusion detection system (IDS) is to monitor the attacks occurring in the computer or networks. Anomaly intrusion detection plays an important role in IDS to detect new attacks by detecting any deviation from the normal profile. In this paper, an intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection is proposed. The key idea is to take the advantage of support vector machine (SVM), decision tree (DT), and simulated annealing (SA). In the proposed algorithm, SVM and SA can find the best selected features to elevate the accuracy of anomaly intrusion detection. By analyzing the information from using KDD'99 dataset, DT and SA can obtain decision rules for new attacks and can improve accuracy of classification. In addition, the best parameter settings for the DT and SVM are automatically adjusted by SA. The proposed algorithm outperforms other existing approaches. Simulation results demonstrate that the proposed algorithm is successful in detecting anomaly intrusion detection.