Trusted Behavior Based Spam Filtering

Abstract

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 trusted 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 designed combining the trusted behavior recognition with Bayesian Analysis. The effectiveness of both the trusted Behavior recognition and the integrated filter are evaluated.