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Keywords: intrusion detection system, classification algorithm, and metaheuristic algorithm.

 

Ze-Hong Chen1, a , Yi-Lin Chen1,b, Wei-Yan Chang1,c , and Chun-Wei Tsai2,d*

1Computer Science and Engineering, National Chung Hsing University, Taiwan, R.O.C.
2Computer Science and Engineering, National Sun Yat-sen University, Taiwan, R.O.C.
a This email address is being protected from spambots. You need JavaScript enabled to view it., b This email address is being protected from spambots. You need JavaScript enabled to view it., c This email address is being protected from spambots. You need JavaScript enabled to view it., d This email address is being protected from spambots. You need JavaScript enabled to view it.


Abstract

An intrusion detection system (IDS), which can be regarded as a subsystem of a network management system, plays the role of detecting and preventing abnormal network behaviors. With the advance of the Internet and the increase of the complexity of network architectures, many attack methods have been developed. However, most traditional intrusion detection systems are incapable of recognizing these attacks. Therefore, this study will present a hybrid classification algorithm for an intrusion detection system to improve its accuracy rate and reduce its computation time. The proposed algorithm integrates k-means (a clustering algorithm), support vector machine (a classification algorithm), and search economic (a metaheuristic algorithm). The experimental results show that the proposed hybrid algorithm provides a better accuracy rate in solving complex network attack classification problems.

 



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