Classification of email using BeaKS: Behavior and keyword stemming

Spam mails are one of the greatest challenges faced by internet service providers, organizations and internet users in unison. Spam mails may be targeted, with a malicious intent or just as a commercial marketing activity - on the whole unwanted by everyone except the dispatcher. Spam filters continuously evolve as spammers go techno-savvy and creative. Machine learning algorithms have been popularly used for classifying and predicting mails as spam or ham (the good emails). This work presents a spam filter, BeaKS, with a focused preprocessing phase that weaves both the content of the email and two behavioral characteristics extracted from the email, to predict the category a mail belongs to: spam or ham. The accuracy of the proposed prediction model using Random Forests as the classifier is shown to be superior over other recent techniques. This approach is simple, easy to implement and reliable.