Fine-tuning large language models for digital forensics: case study and general recommendations
- Large language models (LLMs) have rapidly gained popularity in various fields, including digital forensics (DF), where they offer the potential to accelerate investigative processes. Although several studies have explored LLMs for tasks such as evidence identification, artifact analysis, and report writing, fine-tuning models for specific forensic applications remains underexplored. This paper addresses this gap by proposing recommendations for fine-tuning LLMs tailored to digital forensics tasks. A case study on chat summarization is presented to showcase the applicability of the recommendations, where we evaluate multiple fine-tuned models to assess their performance. The study concludes with sharing the lessons learned from the case study.
| Author: | Gaëtan Michelet, Hans Henseler, Harm van Beek, Mark Scanlon, Frank BreitingerORCiDGND |
|---|---|
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/124192 |
| ISSN: | 2576-5337OPAC |
| Parent Title (English): | Digital Threats: Research and Practice |
| Publisher: | Association for Computing Machinery (ACM) |
| Type: | Article |
| Language: | English |
| Year of first Publication: | 2025 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2025/08/01 |
| DOI: | https://doi.org/10.1145/3748264 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Cybersicherheit | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Latest Publications (not yet published in print): | Aktuelle Publikationen (noch nicht gedruckt erschienen) |
