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

Evaluating Classifiers in Detecting 419 Scams in Bilingual Cybercriminal Communities