Automated generative AI-driven forensic analysis (AGAFA). An explainable neuro-symbolic approach to digital forensics

Graham Parsonage, John Dempsey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Digital Forensics (DF) encompasses the processes of collecting, analysing, and preserving digital evidence crucial for investigations involving cybercrime, security breaches and other criminal cases. The rising number of digital devices requiring investigation, coupled with diminishing confidence in the legal process due to the extensive time needed to process these devices, has prompted the development of a novel framework known as AGAFA. This framework, powered by Explainable AI, aims to meet the growing demand for digital forensic services. The primary objectives of this hybrid neuro-symbolic approach are to enhance the transparency of Artificial Intelligence (AI) in forensic analyses and to protect digital systems. Additionally, it leverages the capabilities of Large Language Models (LLMs) to extract insights from vast datasets in a timely and cost-effective manner. A potential use case for this framework is also illustrated, showcasing its practical application.
Original languageEnglish
Title of host publication2025 International Conference on Software, Knowledge, Information Management & Applications (SKIMA)
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages6
ISBN (Electronic)9781665457347
ISBN (Print)9781665457354
DOIs
Publication statusPublished - 16 Sept 2025
Event16th International Conference on Software, Knowledge, Information Management & Applications - University of the West of Scoltand, Paisley, United Kingdom
Duration: 9 Jun 202511 Jun 2025
https://skimanetwork.org/

Conference

Conference16th International Conference on Software, Knowledge, Information Management & Applications
Abbreviated titleSKIMA 2025
Country/TerritoryUnited Kingdom
CityPaisley
Period9/06/2511/06/25
Internet address

Keywords

  • digital forensics
  • machine learning
  • large language models
  • neuro-symbolic AI
  • explainable AI

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