Identification of Cybercriminals in Social Media using Machine Learning

The significant growth in online social media has allowed users worldwide to communicate freely and share their ideas. The emerging social media is also becoming a powerful communication tool for businesses and organizations. Due to this rapid use of technology, performing crime in newer ways has also emerged. By using social networking sites, criminals can extract the information for criminal activity. Such activities include cyber frauds, cyberbullying, cyberstalking, hacking etc., which may harm an online user. So, identifying such proficient cybercriminals is of utmost importance to mitigate these cyberattacks. With growing technology, these cybercriminals have also become advanced and hide in large underground forums. Majorly, the crime-related activities are in text format, which makes identifying the cybercriminal a tedious task. Detection and discovering the crime and identifying a cybercriminal is the primary task of the crime analysis process. The article proposes a method to solve this problem by combining user-generated content analysis and user network analysis. Machine learning algorithms such as Multinomial Naïve Bayes, K-Nearest Neighbour, Random Forest, Multilayer Perceptron and ID3 can be used. Also, text mining approaches are used to detect and predict criminal activities effectively. These techniques continuously check for suspicious users and create a graph to find the suspected user.