Explainable artificial intelligence for digital forensics

被引:10
|
作者
Hall, Stuart W. [1 ]
Sakzad, Amin [1 ]
Choo, Kim-Kwang Raymond [2 ]
机构
[1] Monash Univ, Fac Informat Technol, Dept Software Syst & Cybersecur, Melbourne, Vic, Australia
[2] Univ Texas San Antonio, Dept Informat Syst & Cyber Secur, San Antonio, TX USA
来源
关键词
artificial intelligence; digital artifacts; digital evidence; digital forensics; EXplainable artificial intelligence;
D O I
10.1002/wfs2.1434
中图分类号
DF [法律]; D9 [法律];
学科分类号
0301 ;
摘要
EXplainable artificial intelligence (XAI) is an emerging research area relating to the creation of machine learning algorithms from which explanations for outputs are provided. In many fields, such as law enforcement, it is necessary that decisions made by and with the assistance of artificial intelligence (AI)-based tools can be justified and explained to a human. We seek to explore the potential of XAI to further enhance triage and analysis of digital forensic evidence, using examples of the current state of the art as a starting point. This opinion letter will discuss both practical and novel ideas as well as controversial points for leveraging XAI to improve the efficacy of digital forensic (DF) analysis and extract forensically sound pieces of evidence (also known as artifacts) that could be used to assist investigations and potentially in a court of law. This article is categorized under: Digital and Multimedia Science > Artificial Intelligence Digital and Multimedia Science > Cybercrime Investigation
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页数:11
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