Modeling Spammer Behavior: Naïve Bayes vs. Artificial Neural Networks
Md. Saiful Islam, Shah Mostafa Khaled, Khalid A. Farhan, Md. Abdur Rahman, Joy Rahman · 2009
Addressing the problem of spam emails in the Internet, this paper presents a comparative study on Nai¿ve 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 Nai¿ve 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.