Machine learning-based criminal behavior analysis for enhanced digital forensics.

ABSTRACT: In an increasingly digital world, uncovering criminal activity often relies on analyzing vast amounts of online behavior. Traditional methods in digital forensics struggle to keep up with the complexity and volume of data, particularly when trying to detect subtle deviations in online activity that could signal illegal intent. This research introduces an innovative approach that leverages machine learning to analyze internet activity-specifically browser artifacts-shedding new light on criminal behaviors that would otherwise remain hidden.Using advanced machine learning techniques such as Long Short-Term Memory (LSTM) networks and Autoencoders, this study focuses on detecting suspicious patterns and anomalies in browsing activity. By understanding the sequence and timing of a user's online

SUBMITTER: Dananjana WP 

PROVIDER: S-EPMC12500087 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature