A Collaborative and Adaptive Intrusion Detection Based on SVMs and Decision Trees
作者： Luyao Teng , Shaohua Teng , Feiyi Tang , Haibin Zhu , Wei Zhang
关键词: Artificial intelligence 、 Data mining 、 Machine learning 、 Network security 、 Intrusion detection system 、 Support vector machine 、 Computer science 、 Feature extraction 、 Decision tree 、 Scheduling (computing)
摘要: Because network security has become one of the most serious problems in world, intrusion detection is an important defence tool security. In this paper, A cooperative and adaptive method proposed a corresponding model designed implemented. The E-CARGO used to build collaborative model. roles, agents groups based on 2-class Support Vector Machines (SVMs) Decision Trees (DTs) are described built, scheduling mechanisms designed. Finally, KDD CUP 1999 data set verify effectiveness our method. Experimental results show that paper superior SVM accuracy efficiency.
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