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.
This work was supported by the National Natural Science Foundation of China under the Grant No. 60575034.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhang, X., Liu, J., Zhang, Y., Wang, C. (2006). Spam Behavior Recognition Based on Session Layer Data Mining. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_160
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DOI: https://doi.org/10.1007/11881599_160
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
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