Protection Against Cyber Crime on Social Media using Adaptive Random Forest Algorithm

Abstract

Internet technology advanced has resulted in cybercrime, security problems, intruders and hackers. Social media platforms have become extremely popular as they are the most effective and efficient way for information to be communicated and shared. More than one billion users are connected via social media, and the lack of sensitivity towards privacy and security causes cybercrime to rise. In order to predict and detect crime, cybercrime investigations are among Data Mining's broad applications. It promotes society and improves life. We analyse cybercrime in social media with the Adaptive Random forest algorithm in this research paper. We compiled the algorithms using WEKA based on the F-measurement value that is accurate. We have also proposed an extensively feasible model that will help us to develop functionalities to classify the threat automatically.