Title :
Comparison of different fusion approaches for network intrusion detection using ensemble of RBFNN
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., China
Abstract :
The information technology has been adopted to solve problems in network intrusion detection system (IDS) and many approaches have been proposed to tackle the information security problems of computer networks, especially the denial of service (DoS) attacks. The multiple classifier system (MCS) is one of the approaches that has been adopted in the detection of DoS attacks recently. For a MCS to perform better than a single classifier, it is crucial to select the appropriate fusion strategies. Majority vote, average, weighted sum, weighted majority vote, neural network and Dempster-Shafer combination are the fusion strategies that have been widely adopted. The selection of the fusion strategy for a MCS in DoS problem varies widely. In this paper, a comparative study on adopting different fusion strategies for a MCS in DoS problem is provided.
Keywords :
computer networks; inference mechanisms; pattern classification; quality of service; radial basis function networks; security of data; Dempster-Shafer theory; denial of service attack; information security; multiple classifier system; network intrusion detection; radial basis function neural network; Computer crime; Computer networks; Electronic mail; Information technology; Intrusion detection; Machine learning; Neural networks; Support vector machine classification; Support vector machines; Voting; Denial of Service attack; Fusion; Multiple Classifier System; Neural Network Ensemble; Radial Basic Function Neural Network;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527610
