Title :
Refinement of rule-based intrusion detection system for denial of service attacks by support vector machine
Author :
Chan, Aki P F ; Ng, Wing W Y ; Yeung, Daniel S. ; Tsang, Eric C C
Author_Institution :
Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China
Abstract :
With the tremendous increase in connectivity and accessibility to the Internet, information security has become a serious global issue. Denial of service (DoS), one of the attacks evolved in recent years, has devastating effect to the commercial activities. We propose a hybrid intrusion detection system (HIDS) which incorporates the benefits of both rule-based and SVM techniques. In brief, the SVM is used to select important features and generate rules, while the rule-based system is then applied to detect the DoS attacks. The rule set generated by the HIDS is more accurate and compact. Experimental results show that the HIDS has a better performance than the rule-based system with rules extracted only from human experts.
Keywords :
Internet; feature extraction; knowledge acquisition; knowledge based systems; learning (artificial intelligence); security of data; support vector machines; Internet; SVM; denial of service attack detection; feature selection; human experts; hybrid intrusion detection system; information security; learning mechanism; rule based system; rule generation; rules extraction; support vector machine; Business; Companies; Computer crime; Computer security; Humans; Information security; Internet; Intrusion detection; Protection; Support vector machines;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1384585