Machine Learning Approaches for Modeling Spammer Behavior

TL;DR

This paper explores machine learning techniques like Naive Bayes, Decision Trees, and SVMs to model spammer behavior, achieving a detection rate of around 92% and improving upon previous methods.

Contribution

It demonstrates the effectiveness of well-known classifiers in modeling spammer patterns, surpassing existing spam detection performance.

Findings

Detection rate of around 92%

Improved performance over previous research

Effective modeling of spammer behavior

Abstract

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.