  Spam is commonly known as unsolicited or unwanted email messages in the
Internet causing potential threat to Internet Security. Users spend a valuable
amount of time deleting spam emails. More importantly, ever increasing spam
emails occupy server storage space and consume network bandwidth. Keyword-based
spam email filtering strategies will eventually be less successful to model
spammer behavior as the spammer constantly changes their tricks to circumvent
these filters. The evasive tactics that the spammer uses are patterns and these
patterns can be modeled to combat spam. This paper investigates the
possibilities of modeling spammer behavioral patterns by well-known
classification algorithms such as Na\"ive Bayesian classifier (Na\"ive Bayes),
Decision Tree Induction (DTI) and Support Vector Machines (SVMs). Preliminary
experimental results demonstrate a promising detection rate of around 92%,
which is considerably an enhancement of performance compared to similar spammer
behavior modeling research.
