  Addressing the problem of spam emails in the Internet, this paper presents a
comparative study on Na\"ive Bayes and Artificial Neural Networks (ANN) based
modeling of spammer behavior. Keyword-based spam email filtering techniques
fall short to model spammer behavior as the spammer constantly changes tactics
to circumvent these filters. The evasive tactics that the spammer uses are
themselves patterns that can be modeled to combat spam. It has been observed
that both Na\"ive Bayes and ANN are best suitable for modeling spammer common
patterns. Experimental results demonstrate that both of them achieve a
promising detection rate of around 92%, which is considerably an improvement of
performance compared to the keyword-based contemporary filtering approaches.
