Artificial intelligence in digital forensics

Paul Reedy

doi:10.1016/B978-0-12-823677-2.00236-1

Artificial intelligence in digital forensics

Paul Reedy

Research output: Chapter in Book/Report/Conference proceeding › Entry for encyclopedia/dictionary

2 Citations (Scopus)

Abstract

Since its inception as a forensic discipline, digital evidence has experienced rapid growth in its contribution to the justice system. Accompanying that growth in utility has been the rapid increase in volume and complexity of the subject evidence. The existent analytical methods do not scale to meet the growing demand and practitioners are looking to artificial intelligence to automate some of the tasks in digital evidence analysis. Machine Learning and Deep Learning are described and the ways in which they contribute to automation and the future possibilities. Included are descriptions for training models, validation, explainability and bias.

Original language	English
Title of host publication	Encyclopedia of Forensic Sciences
Subtitle of host publication	Third Edition
Editors	Max M. Houck
Place of Publication	London
Publisher	Elsevier
Pages	170-192
Number of pages	23
Volume	1
Edition	3rd
ISBN (Electronic)	9780128236789
DOIs	https://doi.org/10.1016/B978-0-12-823677-2.00236-1
Publication status	Published - 2023

Keywords

Artificial intelligence
Deep learning
Digital evidence
Digital forensics
Machine learning
Models

ASJC Scopus subject areas

General Social Sciences

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/B978-0-12-823677-2.00236-1Licence: Elsevier User Licence

Cite this

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KW - Artificial intelligence

KW - Deep learning

KW - Digital evidence

KW - Digital forensics

KW - Machine learning

KW - Models

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ER -

Artificial intelligence in digital forensics

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

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Cite this