Digital Forensic: Techniques, Challenges, and Future Direction

Abstract

Digital forensics is now a crucial field of study in cybersecurity and criminal investigation, which helps identify, analyze, and preserve digital evidence. This systematic literature review discusses the current techniques, challenges, and future directions of research in digital forensics. Contemporary forensic techniques include disk forensics, network forensics, memory forensics, and cloud forensics, with the help of artificial intelligence (AI) and machine learning (ML) to improve evidence identification and analysis. Yet, digital forensics is confronted by various challenges, such as the quick development of technology, encryption intricacies, anti-forensic methods, and the volatility of digital data. Growing reliance on cloud computing, Internet of Things (IoT) devices, and encrypted communication channels only makes forensic examinations more challenging. Also, issues of legal and ethical considerations, like jurisdictional disputes and privacy, hinder the efficacy of forensic procedures. Future research will have to address the development of sophisticated automation methods, trans-border legal instruments, and AI-based forensic software for processing massive amounts of data. Blockchain-based technology for maintaining evidence integrity and normalized forensic processes in jurisdictions can further improve investigation effectiveness. The findings of this study emphasize the imperative of ongoing innovation and convergence among academia, law enforcement agencies, and technology providers to solve evolving digital forensic challenges.

Introduction

Digital forensics is a rapidly evolving field essential for cybersecurity, criminal investigations, and legal proceedings. It involves identifying, preserving, analyzing, and presenting digital evidence. The rise of digital devices, cloud computing, and encrypted communications has increased the complexity of investigations, requiring innovative forensic methods. Current techniques include disk, network, memory, and mobile forensics, but challenges such as anti-forensic tools, legal and jurisdictional issues, and large data volumes persist.

A systematic literature review (2010–2024) was conducted to identify common forensic techniques, major challenges, and future research directions. Key findings highlight the adoption of advanced methods like AI and machine learning for automated evidence analysis, blockchain for securing evidence integrity, and the need for standardized forensic frameworks to address cross-border legal challenges. Emerging threats such as deepfakes, IoT forensics, and quantum computing require further research.

Conclusion

This systematic review of literature discussed digital forensic methods, issues, and future trends through examination of research already conducted in this area. The research categorized numerous forensic techniques, such as disk forensics, memory forensics, network forensics, mobile forensics, and cloud forensics, and noted the shift towards AI-based tools for extracting evidence and detecting anomalies. Although these advances increase forensic potential, digital forensics still confronts numerous challenges, including anti-forensic measures, high-volume data processing, and jurisdictional and legal issues. The review also highlighted new trends such as blockchain for forensic integrity, forensic automation, and the requirement of standardized frameworks to facilitate international cooperation in cybercrime investigations. In spite of the technological progress, digital forensics is still a developing field that needs constant adjustments to meet the new cyber threats. The growing application of encryption, cloud storage, and decentralized systems calls for stronger forensic tools. Legal and ethical aspects, particularly in cross-border investigations, are also major challenges that need international cooperation and harmonized policies.

References

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Copyright

Copyright © 2025 Abdul Sallam Said Hamed, Dr. Omprakash Dewangan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.