An Intelligent Browser History Forensics Method for Automated Analysis of Web Activity Logs, Credentials, and User Behavioral Profiles

In digital forensics, one of the complicated task is analyzing web browser data due to different types of devices, browsers and no updated approaches. Browsers store a large amount of information about user activity because users most often access the internet through them. However, existing approaches to analyzing this browser data still have gaps. One of the main problem developed platforms based on the old methods can not show complete information about the user's activity and have issues with precision. The article discusses the internal architecture of the browser, which is stored in the memory drives inside devices, for instance, computers or mobile devices. The research paper offers solution with developed module based on new method which integrates machine learning algorithms, such as K-NN algorithm and Naive Bayes. The main purpose of the paper it is shows new method which can automatically analyzes browser's data, detects suspicious login activity, and generates user behavior profile. The results show that the proposed new method , on which the developed platform is based, demonstrates user's profile by interests, emotional state and financial state. Also it possible to see list of top visited domain and main user's favorite website categories. It has been found that our methods shows with high accuracy 99.9\% . Also the result of new method , on which the developed platform is based shows the suspicious web-sites and user's logins. Compared to Oxygen Forensics and Nirsoft which less capabilities., the proposed method provides increased accuracy , automated user profiling and detection of suspicious user's activity.