Automation for digital forensics: towards a classification model for the community

  • The current state of automation in digital forensics remains insufficiently defined. While the complexity of automated tools and methods has evolved significantly (e.g., from basic parsers to the integration of advanced techniques), it remains challenging to pinpoint the field’s overall progress or compare methods. A first step towards a solution was the work ‘Automation for digital forensics: Towards a definition for the community’ which defines automation but cannot categorize various methods. This work aims to address this gap and presents a first classification model for automation for digital forensics. Therefore, we analyzed automation classification schemes from different disciplines (e.g., cars) and assessed various model possibilities as well as characteristics. We conclude that a 2-dimensional model with the axis ‘decision’ and ‘level of automation’ is most appropriate and provide an overview table with examples.

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Metadaten
Author:Gaëtan Michelet, Frank BreitingerORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1213463
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/121346
ISBN:979-8-4007-1076-6OPAC
Parent Title (English):DFDS '25: Proceedings of the Digital Forensics Doctoral Symposium, Brno, Czech Republic, 1 April 2025
Publisher:Association for Computing Machinery (ACM)
Place of publication:New York, NY
Type:Conference Proceeding
Language:English
Year of first Publication:2025
Publishing Institution:Universität Augsburg
Release Date:2025/04/10
First Page:8
DOI:https://doi.org/10.1145/3712716.3712725
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
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)