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

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  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
      Machine Learning* ; Criminal Behavior* ; Forensic Sciences*/methods ; Criminals*; Humans ; Internet ; Crime
    • Abstract:
      Competing Interests: The authors have declared that no competing interests exist.
      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 actions, this method enhances digital forensics investigations, allowing for faster and more accurate detection of criminal intent and behavior. The research aims to improve the speed and accuracy of identifying malicious online activity but also offers law enforcement and investigators a powerful tool to make sense of complex data. These findings represent an important step towards advancing digital forensics, enabling deeper insights into criminal behavior and more effective investigations, ultimately contributing to a safer digital environment.
      (Copyright: © 2025 Pawani Dananjana et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
    • References:
      Front Psychol. 2018 May 15;9:691. (PMID: 29867661)
      Sensors (Basel). 2023 Apr 28;23(9):. (PMID: 37177554)
      Front Big Data. 2024 May 09;7:1375818. (PMID: 38784677)
    • Publication Date:
      Date Created: 20251006 Date Completed: 20251006 Latest Revision: 20251009
    • Publication Date:
      20251009
    • Accession Number:
      PMC12500087
    • Accession Number:
      10.1371/journal.pone.0332802
    • Accession Number:
      41052218