Spam behavior recognition based on session layer data mining

Authors:
Xuan Zhang;Jianyi Liu;Yaolong Zhang;Cong Wang
Affiliations:
School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China
Venue:
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Year:
2006

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Abstract

Various approaches are presented to solve the growing spam problem. However, most of these approaches are inflexible to adapt to spam dynamically. This paper proposes a novel approach to counter spam based on spam behavior recognition using Decision Tree learned from data maintained during transfer sessions. A classification is set up according to email transfer patterns enabling normal servers to detect malicious connections before mail body delivered, which contributes much to save network bandwidth wasted by spams. An integrated Anti-Spam framework is founded combining the Behavior Classification with a Bayesian classification. Experiments show that the Behavior Classification has high precision rate with acceptable recall rate considering its bandwidth saving feature. The integrated filter has a higher recall rate than either of the sub-modules, and the precision rate remains quite close to the Bayesian Classification.