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

fYear

2014

fDate

14-14 Dec. 2014

Firstpage

898

Lastpage

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.

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);

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining Workshop (ICDMW), 2014 IEEE International Conference on

Conference_Location

Shenzhen

Print_ISBN

978-1-4799-4275-6

Type

conf

DOI

10.1109/ICDMW.2014.147

Filename

7022692