Version 1
: Received: 18 March 2024 / Approved: 19 March 2024 / Online: 19 March 2024 (10:21:02 CET)
How to cite:
Kassa, Y.W.; James, J.I.; Belay, E.G. Intention Recognition in Digital Forensics: Systematic Review. Preprints2024, 2024031086. https://doi.org/10.20944/preprints202403.1086.v1
Kassa, Y.W.; James, J.I.; Belay, E.G. Intention Recognition in Digital Forensics: Systematic Review. Preprints 2024, 2024031086. https://doi.org/10.20944/preprints202403.1086.v1
Kassa, Y.W.; James, J.I.; Belay, E.G. Intention Recognition in Digital Forensics: Systematic Review. Preprints2024, 2024031086. https://doi.org/10.20944/preprints202403.1086.v1
APA Style
Kassa, Y.W., James, J.I., & Belay, E.G. (2024). Intention Recognition in Digital Forensics: Systematic Review. Preprints. https://doi.org/10.20944/preprints202403.1086.v1
Chicago/Turabian Style
Kassa, Y.W., Joshua Isaac James and Elefelious Getachew Belay. 2024 "Intention Recognition in Digital Forensics: Systematic Review" Preprints. https://doi.org/10.20944/preprints202403.1086.v1
Abstract
In this comprehensive review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Whereas, Intention recognition is a subfield of artificial intelligence that deals with the identification of agents’ intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a meticulous six-step systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, AI black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses unique challenges in the digital forensics landscape. We advocate for hybrid solutions that blend deep learning’s power with interpretability. Furthermore, we propose the creation of a comprehensive taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field.
Keywords
Digital Forensics; Intention Recognition; Goal Recognition; plan recognition; cyberattack; cybercrime; model
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
