CRIMINAL DATA CLASSIFICATION IN CYBERCRIME INVESTIGATION USING WORDNET AND MODIFIED NAÏVE BAYES CLASSIFIER

computer engineering

Cybercriminals exploit the opportunities provided by the information revolution and social media to communicate and conduct underground illicit activities, such as online fraudulence, cyber pre-dation, cyberbullying, hacking, blackmailing, and drug smuggling. To combat the increasing number of criminal activities, structure and content analysis of criminal communities can provide insight and facilitate cybercrime forensics. In this paper, we propose a framework to analyze chat logs for crime investigation using data mining and natural language processing techniques. The proposed framework extracts the social network from chat logs and summarizes conversation into topics. The crime investigator can use information visualizer to see the crime-related results. To test the validity of our proposed framework, we worked in a joint effort with the cybercrime unit of a Canadian law enforcement agency. The experimental outcomes on real-life data and feedback from the law enforcement of cers suggest that the proposed chat log mining framework meets the need for law enforcement agencies and is very effective for crime investigation.

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