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2014 IEEE International Conference on Data Mining Workshop (ICDMW) (2014)
Shenzhen, China
Dec. 14, 2014 to Dec. 14, 2014
ISBN: 978-1-4799-4275-6
pp: 898-905
ABSTRACT
Because network security has become one of the most serious problems in the world, intrusion detection is an important defence tool of network security. In this paper, A cooperative and adaptive intrusion detection method is proposed and a corresponding intrusion detection model is designed and implemented. The E-CARGO model is used to build the collaborative and adaptive intrusion detection model. The roles, agents and groups based on 2-class Support Vector Machines (SVMs) and Decision Trees (DTs) are described and built, and the adaptive scheduling mechanisms are designed. Finally, the KDD CUP 1999 data set is used to verify the effectiveness of our method. Experimental results show that the collaborative and adaptive intrusion detection method proposed in this paper is superior to the detection of the SVM in the detection accuracy and detection efficiency.
INDEX TERMS
Intrusion detection, Support vector machines, Collaboration, Adaptation models, Detectors, Feature extraction, Generators
CITATION

L. Teng et al., "A Collaborative and Adaptive Intrusion Detection Based on SVMs and Decision Trees," 2014 IEEE International Conference on Data Mining Workshop (ICDMW), Shenzhen, China, 2014, pp. 898-905.
doi:10.1109/ICDMW.2014.147
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