NDLI: A Collaborative and Adaptive Intrusion Detection Based on SVMs and Decision Trees

Content Provider	IEEE Xplore Digital Library
Author	Teng, Luyao Teng, Shaohua Tang, Feiyi Zhu, Haibin Zhang, Wei Liu, Dongning Liang, Lu
Copyright Year	2014
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.
Starting Page	898
Ending Page	905
File Size	221532
Page Count	8
File Format	PDF
e-ISBN	9781479942749
DOI	10.1109/ICDMW.2014.147
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intrusion Detection Adaptation models Group Agent Decision Tree Support Vector Machine (SVM) Collaborative Generators Support vector machines Role Adaptive Intrusion detection Collaboration Detectors Feature extraction
Content Type	Text
Resource Type	Article

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