Machine Learning-Driven Forensics and Evidence Analysis for Cybercrime Investigations

Authors

  • Amtabh Srivastava,Dr. Jitender Rai

Keywords:

Cybercrime Forensics, Machine Learning, Digital Evidence Analysis, ML Investigative Tools

Abstract

The increasing complexity and volume of cybercrimes necessitate innovative forensic techniquescapable of handling multifaceted digital evidence. This paper explores how machine learning (ML), a subdomainof artificial intelligence (AI)

References

Iqbal, F., Debbabi, M., Fung, B. C., Iqbal, F., Debbabi, M., & Fung, B. C. (2020). Artificial intelligence and digital

forensics. Machine learning for authorship attribution and cyber forensics, 139-150.

Jeong, D. (2020). Artificial intelligence security threat, crime, and forensics: taxonomy and open issues. IEEE Access, 8,

-184574.

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Published

2025-05-31

How to Cite

Amtabh Srivastava,Dr. Jitender Rai. (2025). Machine Learning-Driven Forensics and Evidence Analysis for Cybercrime Investigations . Journal of Computational Analysis and Applications (JoCAAA), 34(5), 296–310. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2933

Issue

Vol. 34 No. 5 (2025): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.