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

W Pawani Dananjana1, Jithmi Sewwandi Arambawela1, D G Samesha Navodi Gonawala1

  • 1Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka.

Plos One
|October 6, 2025
PubMed
Summary
This summary is machine-generated.

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A cost effective machine learning based network intrusion detection system using Raspberry Pi for real time analysis.

PloS one·2025
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This study uses machine learning to analyze browser history for criminal activity detection. It enhances digital forensics by identifying suspicious online behavior patterns for faster investigations.

Area of Science:

  • Digital Forensics
  • Cybersecurity
  • Machine Learning

Background:

  • Traditional digital forensics methods struggle with the volume and complexity of online data.
  • Detecting subtle deviations in online behavior to identify criminal intent is challenging.
  • There is a need for advanced analytical tools in digital investigations.

Purpose of the Study:

  • To introduce a novel machine learning approach for analyzing internet activity.
  • To enhance the detection of criminal behavior through browser artifact analysis.
  • To improve the speed and accuracy of identifying malicious online activity.

Main Methods:

  • Utilized advanced machine learning techniques, including Long Short-Term Memory (LSTM) networks and Autoencoders.
  • Focused on analyzing sequences and timing of user online actions within browser artifacts.

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  • Developed a method to detect suspicious patterns and anomalies in internet browsing data.

    • Main Results:

    • Successfully identified subtle deviations in online behavior indicative of criminal intent.
    • Demonstrated the capability of machine learning models to detect anomalies in browsing activity.
    • Enhanced the potential for uncovering hidden criminal behaviors through data analysis.

    Conclusions:

    • Machine learning offers a powerful tool for advancing digital forensics.
    • The proposed method improves the accuracy and efficiency of identifying malicious online activity.
    • This research contributes to a safer digital environment by providing investigators with better tools.