Detecting Threat E-mails using Bayesian Approach

Fraud and terrorism have a close connect in terms of the processes that enables and promote them. In the era of Internet, its various services that include Web, e-mail, social networks, blogs, instant messaging, chats, etc. are used in terrorism not only for communication but also for i) creation of ideology, ii) resource gathering, iii) recruitment, indoctrination and training, iv) creation of terror network, and v) information gathering. A major challenge for law enforcement and intelligence agencies is efficient and accurate gathering of relevant and growing volume of crime data. This paper reports on use of established Naïve Bayesian filter for classification of threat e-mails. Efficiency in filtering threat e-mail by use of three different Naïve Bayesian filter approaches i.e. single keywords, weighted multiple keywords and weighted multiple keywords with keyword context matching are evaluated on a threat e-mail corpus created by extracting data from sources that are very close to terrorism.