A hybrid approach to combat email-based cyberstalking

Abstract

Email is one of the most popular Internet applications which enables individuals and organisations alike to communicate and work effectively. However, email has also been used by criminals as a means to commit cybercrimes such as phishing, spamming, cyberbullying and cyberstalking. Cyberstalking is a relatively new surfacing cybercrime, which recently has been recognised as a serious social and worldwide problem. Combating email-based cyberstalking is a challenging task that involves two crucial steps: a robust method for filtering and detecting cyberstalking emails and documenting evidence for identifying cyberstalkers as a prevention and deterrence measure. In this paper, we discuss a hybrid approach that applies machine learning to detect, filter and file evidence. To this end we present a new robust feature selection approach to select informative features, aiming to improve the performance of machine learning within this task.