Title :
A Collaborative and Adaptive Intrusion Detection Based on SVMs and Decision Trees
Author :
Luyao Teng ; Shaohua Teng ; Feiyi Tang ; Haibin Zhu ; Wei Zhang ; Dongning Liu ; Lu Liang
Author_Institution :
Coll. of Eng. & Sci., Victoria Univ. Ballarat Rd, Footscray, VIC, Australia
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.
Keywords :
decision trees; scheduling; security of data; support vector machines; DT; E-CARGO model; SVM; decision tree; intrusion detection; network security; scheduling mechanism; support vector machine; Adaptation models; Collaboration; Detectors; Feature extraction; Generators; Intrusion detection; Support vector machines; Adaptive; Agent; Collaborative; Decision Tree; Group; Intrusion Detection; Role; Support Vector Machine (SVM);
Conference_Titel :
Data Mining Workshop (ICDMW), 2014 IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4799-4275-6
DOI :
10.1109/ICDMW.2014.147
