PriMera Scientific Engineering (ISSN: 2834-2550)

Research Study

Volume 8 Issue 1

Using Artificial Intelligent Techniques to Standardize and Automate the Generation of Digital Forensic Reports

Idani Mulaudzi* and Hein Venter

January 05, 2026

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Abstract

The present study investigates if the digital forensics report can be generated automatically by using some of the artificial intelligence techniques, specifically the natural language processing. A model has been developed to assess if it is feasible to automate the generation of a digital forensic report using artificial intelligent techniques. One of the main purposes for this study is coming from a point where human errors, structure of the digital forensic reports, critical evidence that should take part of the digital forensic report are omitted during the generation of digital forensic report as well as the interpretation of the evidence drafted by an investigator during investigation. In addition, the standardization of this report happens to be imminent especially when it is being presented in a court of law. Given the rise of cybercrime, more research is needed to better improve the process of automating the generating digital forensic report using some intelligent techniques.

Keywords: digital forensics; artificial intelligence (AI); natural Language processing (NLP); digital forensic report (DF report); standardization

References

  1. Adeyanju IA., et al. “Machine learning methods for sign language recognition: A critical review and analysis”. Intelligent Systems with Applications 12 (2021): 200056.
  2. Awan SA., et al. “Digital Forensics and Cyber Forensics Investigation: security challenges, limitations, pen issues, and future direction”. International Journal of Electronic Security and Digital Forensics 1.1 (2022): 1.
  3. Buhalis D and Law R. “Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research”. Tourism Management 29.4 (2008): 609-623.
  4. Casey E and Stellatos GJ. “The impact of full disk encryption on digital forensic”. Operating Systems Review 42.3 (2008): 93-98.
  5. Chan AW., et al. “SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials”. BMJ 346 (2013): e7586.
  6. Collobert Ronan., et al. “Natural language processing (Almost) from scratch” Journal of Machine Learning Research (2011).
  7. Costantini S, De Gasperis G and Olivieri R. “Digital forensics and investigations meet artificial intelligence”. Annals of Mathematics and Artificial Intelligence 86.1-3 (2019): 193-229.
  8. De Alwis C., et al. “Survey on 6G frontiers: trends, applications, requirements, technologies and future research”. IEEE Open Journal of the Communications Society 2 (2021): 836-886.
  9. Fahlevi M., et al. “Cybercrime Business digital in Indonesia”. E3S Web of Conferences 125 (2019): 21001.
  10. Fuchs K. “Exploring the opportunities and challenges of NLP models in higher education: is Chat GPT a blessing or a curse”. Frontiers in Education (2023): 8.
  11. Goh OS., et al. “Top-down natural language query approach for embodied conversational agent”. International Multiconference of Engineers and Computer Scientists (2006): 470-475.
  12. Hirschberg J and Manning CD. “Advances in natural language processing”. Science 349.6245 (2015): 261-266.
  13. Roussev V. “Hashing and data fingerprinting in digital forensics” IEEE Security & Privacy 7.2 (2009): 49-55.
  14. Shahbazi Z and Byun YC. “NLP-Based Digital Forensic Analysis for Online Social Network based on System Security”. International Journal of Environmental Research and Public Health/International Journal of Environmental Research and Public Health 19.12 (2022): 7027.
  15. Shalaginov A, Iqbal A and Olegård J. “IoT Digital Forensics Readiness in the Edge: A Roadmap for Acquiring Digital Evidence from Intelligent Smart Applications”. in Lecture notes in computer science (2020): 1-17.
  16. Siponen MT. “A conceptual foundation for organizational information security awareness”. Information Management & Computer Security 8.1 (2000): 31-41.
  17. Van Niekerk JF Von Solms R. “Information security culture: A management perspective”. Computers & Security 29.4 (2010): 476-486.
  18. Von Solms R and Van Niekerk J. “From information security to cyber security”. Computers & Security 38 (2013a): 97-102.
  19. Artificial intelligence in online shopping using natural language processing (nlp)'. Journal of Critical Reviews 7.4 (2020).
  20. Kothapalli S and Appavu R. “The application of artificial intelligence and machine learning to anesthesiology”. Journal of Student Research 12.2 (2023).
  21. SANS 27043, information technology - Security techniques - incident investigation principles and processes.
  22. SANS 27037, Information technology — Security techniques — Guidelines for identification, collection, acquisition, and preservation of digital evidence.