Evaluating Classifiers in Detecting 419 Scams in Bilingual Cybercriminal Communities

Incidents of organized cybercrime are rising because of criminals are reaping high financial rewards while incurring low costs to commit crime. As the digital landscape broadens to accommodate more internet-enabled devices and technologies like social media, more cybercriminals who are not native English speakers are invading cyberspace to cash in on quick exploits. In this paper we evaluate the performance of three machine learning classifiers in detecting 419 scams in a bilingual Nigerian cybercriminal community. We use three popular classifiers in text processing namely: Na"ive Bayes, k-nearest neighbors (IBK) and Support Vector Machines (SVM). The preliminary results on a real world dataset reveal the SVM significantly outperforms Na"ive Bayes and IBK at 95% confidence level.