Digital forensics has emerged in the scene as a necessary area in the present scenario of cybersecurity, due to the
rising trend of dependence on digital infrastructure that is vulnerable to complex cyber-attacks. The rising level of digital
evidence and its complexity has made it challenging for traditional methods of investigation to be efficient and scalable. In
this context, Artificial Intelligence has emerged in the scene as a promising solution that can assist in enhancing forensic
practices by facilitating fast data analysis, complex pattern analysis, and efficient analysis of large datasets. This will assist in
the efficient identification of potential security incidents and will assist in enhancing forensic practices as a whole. However,
the application of AI in digital forensics also has some critical points to be considered. The concerns of data integrity,
transparency, privacy, and admissibility of evidence in courts imply that the developments in technology are not sufficient to
ensure the accuracy and integrity of the same. The requirement of human intelligence in interpreting the findings,
understanding the context, and ensuring that the forensic evidence is credible and defensible is required in this context. This
paper explores the effects of AI on the evolution of digital forensic methods and also explores the challenges that are
associated with the use of AI in digital forensic analysis. This paper explores the importance of achieving a balance where
intelligent technology can support human expertise, leading to more efficient and accountable forensic processes in the
digital era.
[1] E. Casey, Digital Evidence and Computer Crime: Forensic Science, Computers, and the Internet. Amsterdam, Netherlands:
Academic Press, 2011.
[2] B. Carrier, File System Forensic Analysis. Boston, MA, USA: Addison-Wesley Professional, 2005.
[3] S. L. Garfinkel, “Digital forensics research: The next 10 years,” Digital Investigation, vol. 7, pp. S64–S73, 2010.
[4] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ, USA: Prentice Hall, 1995.
[5] I. Goodfellow, Y. Bengio and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
[6] P. Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. New
York, NY, USA: Basic Books, 2015.
[7] T. M. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.
[8] M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science, vol. 349, no. 6245, pp.
255–260, 2015.
[9] B. Carrier and E. H. Spafford, “Getting physical with the digital investigation process,” International Journal of Digital
Evidence, vol. 2, no. 2, pp. 1–20, 2003.
[10] P. W. Singer and A. Friedman, Cybersecurity and Cyberwar: What Everyone Needs to Know. Oxford, U.K.: Oxford
University Press, 2013.
[11] D. Quick and K. K. R. Choo, “Digital forensic intelligence: Data subsets and Open Source Intelligence (DFINT +
OSINT),” Future Generation Computer Systems, vol. 78, pp. 558–567, 2018.
[12] S. Raghavan, “Digital forensic research: Current state of the art,” CSI Transactions on ICT, vol. 1, no. 1, pp. 91–114, 2013.
[13] A. L. Buczak and E. Guven, “A survey of data mining and machine learning methods for cybersecurity intrusion detection,”
IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1153–1176, 2016.
[14] M. A. Ferrag, L. Maglaras, S. Moschoyiannis and H. Janicke, “Deep learning for cyber security intrusion detection:
Approaches, datasets, and comparative study,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1347–1380, 2020.
[15] R. Sommer and V. Paxson, “Outside the closed world: On using machine learning for network intrusion detection,” in Proc.
IEEE Symposium on Security and Privacy, 2010, pp. 305–316.
[16] J. A. Kroll et al., “Accountable algorithms,” University of Pennsylvania Law Review, vol. 165, no. 3, pp. 633–705, 2017.
[17] I. H. Sarker, “Machine learning: Algorithms, real-world applications and research directions,” SN Computer Science, vol. 2,
no. 3, pp. 1–21, 2021.
[18] H. Chen, R. H. Chiang and V. C. Storey, “Business intelligence and analytics: From big data to big impact,” MIS Quarterly,
vol. 36, no. 4, pp. 1165–1188, 2012.
[19] S. Mohurle and M. Patil, “A brief study of WannaCry threat: Ransomware attack 2017,” International Journal of Advanced
Research in Computer Science, vol. 8, no. 5, 2017.
[20] Digital Forensic Research Workshop (DFRWS), “A roadmap for digital forensic research,” Digital Forensic Research
Workshop, 2001.
[21] A. Behl and K. Behl, Cybersecurity and Cyberwar: What Everyone Needs to Know. Oxford, U.K.: Oxford University Press,
2017.
[22] S. Shinde, A. Kulkarni and S. Bhosale, “Cyber threat intelligence: Survey and research directions,” International Journal
of Computer Applications, vol. 179, no. 46, pp. 20–24, 2018.
