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

Authors

Victor R. Kebande
Richard A. Ikuesan
Nickson M. Karie, Edith Cowan UniversityFollow
Sadi Alawadi
Kim-Kwang Raymond Choo
Arafat Al-Dhaqm

Document Type

Journal Article

Publication Title

Forensic Science International: Reports

Volume

2

Publisher

Elsevier

School

School of Science

RAS ID

32358

Comments

Kebande, V. R., Ikuesan, R. A., Karie, N. M., Alawadi, S., Choo, K. K. R., & Al-Dhaqm, A. (2020). Quantifying the need for supervised machine learning in conducting live forensic analysis of emergent configurations (ECO) in IoT environments. Forensic Science International: Reports, 2, article 100122. https://doi.org/10.1016/j.fsir.2020.100122

Abstract

© 2020 The Author(s) 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 classical machine learning techniques, such as K-Nearest Neigbour, Support Vector Machines, Naive Bayes and Random Forest algorithms, in performing live digital forensics for different IoT configurations. There are also a number of challenges associated with the use of machine learning techniques, as discussed in this paper.

DOI

10.1016/j.fsir.2020.100122

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.