Е-Публикации
Технически университет - София

Abstract: Conducting digital forensic investigations (DFIs) quickly, accurately and efficiently can be accomplished by knowing and using modern technologies, including those typical for machine learning (ML) and artificial intelligence (AI). Therefore, the purpose of the paper is to present an exploration regarding the scientific research on the applicability of ML and AI in DFI, how far and in what cases it can support the work of investigators in the different steps of their adopted methodology. A bibliometric analysis is used to outline the general picture and then a discussion regarding relevant articles is performed in detail. The findings show the potential of ML and AI to be used as tools, for example, to improve timeline reconstruction, to find and recover evidence, in secure chain custody, and others. Also, the researchers state that emerging technologies, including ChatGPT must be carefully examined and used in DFI practice.

References

1. N. Sunde, "Strategies for safeguarding examiner objectivity and evidence reliability during digital forensic investigations," Forensic Science International: Digital Investigation 40, 2022. https://doi.org/10.1016/j.fsidi.2021.301317.
2. A. S. Barkah, S. R. Selamat, Z. Z. Abidin, "Comparative Study of Digital Forensic Investigation on Cyber Criminal," International Journal of Advanced Trends in Computer Science and Engineering, 8(1.5), 2019, 370-374. DOI: 10.30534/ijatcse/2019/6081.52019.
3. K. Mira, F. Bugiotti, T. Morosuk, "Artificial Intelligence and Machine Learning in Energy Conversion and Management," Energies 2023, 16, 7773. https://doi.org/10.3390/en16237773.
4. D. Kuntz, A. K. Wilson, "Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory," Pure and Applied Chemistry, 94(8), 2022, 1019-1054, DOI: Https://doi.org/10.1515/pac-2022-0202.
5. Z. A. Al-Sai, R. Abdullah, and M. H. Husin, "Critical Success Factors for Big Data: A Systematic Literature Review," IEEE Access, vol. 8, 2020, 118940-118956. doi: 10.1109/ACCESS.2020.3005461.
6. M. Aria, and C. Cuccurullo, "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, 11(4), 2017, 959-975. DOI: 10.1016/j.joi.2017.08.007.
7. B. Fakiha, "Enhancing Cyber Forensics with AI and Machine Learning: A Study on Automated Threat Analysis and Classification," International Journal of Safety and Security Engineering, 13(4), 2023, 701-707. https://doi.org/10.18280/ijsse.130412.
8. U. Islam et al. "Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data," Sensors 2023, 23, 5626. https://doi.org/10.3390/s23125626.
9. S. A. Johnson, and S. Ananthakumaran, "Smart Digital Forensic Framework for Crime Analysis and Prediction using AutoML," International Journal of Advanced Computer Science and Applications, 12(3), 2021, 412-420. DOI: 10.14569/IJACSA.2021.0120349.
10. X. Du, and M. Scanlon, "Methodology for the Automated Metadata-Based Classification of Incriminating Digital Forensic Artefacts," 14th International Conference on Availability, Reliability and Security ARES'19, 2019, 1-8. https://doi.org/10.1145/3339252.3340517.
11. Z. Shahbazi, and Y-C. Byun, "NLP-Based Digital Forensic Analysis for Online Social Network Based on System Security,". International Journal of Environmental Research and Public Health, 19(12):7027, 2022. https://doi.org/10.3390/ijerph19127027.
12. E. Irshad, and A. B. Siddiqui, "Cyber threat attribution using unstructured reports in cyber threat intelligence," Egyptian Informatics Journal, 24(1), 2023, 43-59. https://doi.org/10.1016/j.eij.2022.11.001.
13. X. Du et al., "SoK: Exploring the State of the Art and the Future Potential of Artificial Intelligence in Digital Forensic Investigation, " 15th International Conference on Availability, Reliability and Security, ARES 2020, ACM International Conference Proceeding Series, 2020. DOI: 10.1145/3407023.3407068.
14. I. Tsimperidis, D. Grunova, S. Roy, L. Moussiades, "Keystroke Dynamics as a Language Profiling Tool: Identifying Mother Tongue of Unknown Internet Users," Telecom, 4(3), 2023; 369-377. https://doi.org/10.3390/telecom4030021.
15. Z. Baig, M. A. Khan, N. Mohammad, G. B. Brahim, "Drone Forensics and Machine Learning: Sustaining the Investigation Process," Sustainability, 14(8), 2022, 4861. https://doi.org/10.3390/su14084861.
16. A. A. AL-Marghilani, "Target Detection Algorithm in Crime Recognition Using Artificial Intelligence," Computers, Materials & Continua, 71(1), 2022, 809-824. DOI:10.32604/cmc.2022.021185.
17. M. Scanlon, F. Breitinger, C. Hargreaves, J.-N. Hilgert, and J. Sheppard, "ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The Unknown," Forensic Science International: Digital Investigation, vol. 46, 2023. https://doi.org/10.1016/j.fsidi.2023.301609.
18. D. Dunsin, M. C. Ghanem, K. Ouazzane, and V. Vassilev, "A comprehensive analysis of the role of artificial intelligence and machine learning in modern digital forensics and incident response," Forensic Science International: Digital Investigation, 48, 2024. https://doi.org/10.1016/j.fsidi.2023.301675.
19. F. Uhlig at el., "AI and AM e Improving approximate matching through transformer networks," Forensic Science International: Digital Investigation, 45, 2023, 301570. https://doi.org/10.1016/j.fsidi.2023.301570.
20. A. Djenna, E. Barka, A. Benchikh, K. Khadir, "Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics," Sensors, 23(14), 2023, 6302. https://doi.org/10.3390/s23146302.

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