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  5. SoK: Exploring the State of the Art and the Future Potential of Artificial Intelligence in Digital Forensic Investigation
 
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SoK: Exploring the State of the Art and the Future Potential of Artificial Intelligence in Digital Forensic Investigation

Author(s)
Du, Xiaoyu  
Hargreaves, Chris  
Sheppard, John  
Anda, Felix  
Sayakkara, Asanka P.  
Le-Khac, Nhien-An  
Scanlon, Mark  
Uri
http://hdl.handle.net/10197/24771
Date Issued
2020-08-28
Date Available
2023-09-19T15:04:20Z
Abstract
Multi-year digital forensic backlogs have become commonplace in law enforcement agencies throughout the globe. Digital forensic investigators are overloaded with the volume of cases requiring their expertise compounded by the volume of data to be processed. Artificial intelligence is often seen as the solution to many big data problems. This paper summarises existing artificial intelligence based tools and approaches in digital forensics. Automated evidence processing leveraging artificial intelligence based techniques shows great promise in expediting the digital forensic analysis process while increasing case processing capacities. For each application of artificial intelligence highlighted, a number of current challenges and future potential impact is discussed.
Type of Material
Conference Publication
Publisher
ACM
Copyright (Published Version)
2020 the Authors
Subjects

Digital forensics

Machine learning

Deep learning

DOI
10.1145/3407023.3407068
Web versions
https://www.ares-conference.eu
Language
English
Status of Item
Peer reviewed
Journal
ARES '20: Proceedings of the 15th International Conference on Availability, Reliability and Security
Conference Details
The 15th International Conference on Availability, Reliability and Security (ARES 2020), Virtual Conference, 28 August 2020
ISBN
978-1-4503-8833-7
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by/3.0/ie/
File(s)
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SoK-AI-Forensics.pdf

Size

570.73 KB

Format

Adobe PDF

Checksum (MD5)

85dd6beca05adc0631e2d0a257bcaa32

Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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