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In the modern age of content, web browsers serve as an important bridge between content and users. On the other hand, web browsers are also a link to malicious codes, and new types of malicious codes that are secretly mining through browsers are being created. In recent years, a web browser investigation is essential to find the path of malicious code infection when a security incident occurs. However, the content became vast, and as network performance is getting better, websites are getting bigger, and the number of web pages included in a single website has increased exponentially. It is almost impossible to manually analyze all of the thousands of sites due to time limitations. In this paper, we propose a method to apply machine learning to web browser forensics to solve these problems. Also, we propose AIBFT: Artificial Intelligence Browser Forensic Toolkit, a Proof-of-Concept (POC) tool that provides …