Quantifying the need for supervised machine learning in conducting live forensic analysis of emergent configurations (ECO) in IoT environments

Quantifying the need for supervised machine learning in conducting live forensic analysis of emergent configurations (ECO) in IoT environments

Machine learning has been shown as a promising approach to mine larger datasets, such as those that comprise data from a broad range of Internet of Things devices, across complex environment(s) to solve different problems. This paper surveys existing literature on the potential of using supervised c...

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Bibliographic Details
Main Authors: Victor R. Kebande, Richard A. Ikuesan, Nickson M. Karie, Sadi Alawadi, Kim-Kwang Raymond Choo, Arafat Al-Dhaqm
Format: Article
Language:English
Published: Elsevier 2020-12-01
Series:Forensic Science International: Reports
Subjects:
Supervised machine
Learning
Live forensics
Emergent configurations
IoT
Online Access:http://www.sciencedirect.com/science/article/pii/S2665910720300712

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http://www.sciencedirect.com/science/article/pii/S2665910720300712

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