Combining behavior and Bayesian Chinese spam filter
Chengcheng Li, Jianyi Liu · 2009
Various approaches are presented to solve the spreading spam problem. However, most of these approaches can not flexibly and dynamically adapt to spam. This paper proposes a novel approach to counter spam based on spam behavior recognition during transfer sessions. A behavior recognition of email transfer patterns which enables normal servers to detect malicious connections before email body delivered, contributes much to save network bandwidth wasted by spam emails. An integrated anti-spam framework is founded combining the behavior recognition with Bayesian analysis. The effectiveness of both the Behavior recognition and the integrated filter are evaluated.