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Research Paper Volume 8 Issue 4 2597 - 2604 September 1, 2025

Digital Crime Scene Investigation: The Rise of Cybercrime Analytics

Lead author · Corresponding
Mrs. J. Santhi Bai
Lecturer in Commerce at SRR&CVR Government Degree College(A), Vijayawada, Andhra Pradesh, India
Co-author
Mrs. KSG. Sucharitha
Lecturer in Commerce at SRR&CVR Government Degree College(A), Vijayawada, Andhra Pradesh, India
View PDF Full text DOIhttps://doij.org/10.10000/IJLMH.1110704
Abstract

Digital Crime Scene Investigation (DCSI) represents a modern adaptation of traditional forensic methodologies to the virtual realm, addressing the complex challenges posed by cybercrime. With the exponential rise in digital threats, organizations and law enforcement agencies have turned to cybercrime analytics, artificial intelligence (AI), and advanced forensic tools to detect, preserve, and analyze digital evidence effectively. This paper explores the structured principles of DCSI, the role of AI in automating evidence processing and anomaly detection, the growing importance of cloud and Internet of Things (IoT) forensics, and the emerging trends shaping the future of cyber investigations. It highlights key challenges such as data privacy regulations, encryption barriers, jurisdictional complexities, and the shortage of skilled forensic professionals. Additionally, it emphasizes the need for a proactive approach to cybersecurity, shifting from reactive incident handling to continuous threat hunting and forensic readiness. The integration of AI, big data analytics, and global law enforcement collaboration stands out as a transformative force, enhancing both the speed and precision of cybercrime investigations. By adopting these strategies, stakeholders can not only improve investigative efficiency but also fortify their defenses against evolving digital threats.

Type
Research Paper
Information
International Journal of Law Management and Humanities, Volume 8, Issue 4, Page 2597 - 2604
DOI: https://doij.org/10.10000/IJLMH.1110704
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CC BY-NC 4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting, and building upon the work for non-commercial use, provided the original work is properly cited.
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Copyright © IJLMH 2026
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The views and opinions expressed in this manuscript are those of the author(s) alone and do not reflect the views, policies, or position of the Journal.

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