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.
RAS ID
32358
Document Type
Journal Article
Date of Publication
2020
Volume
2
School
School of Science
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Publisher
Elsevier
Recommended Citation
Kebande, V. R., Ikuesan, R. A., Karie, N. M., Alawadi, S., Choo, K., & Al-Dhaqm, A. (2020). Quantifying the need for supervised machine learning in conducting live forensic analysis of emergent configurations (ECO) in IoT environments. DOI: https://doi.org/10.1016/j.fsir.2020.100122
Included in
Computer Sciences Commons, Defense and Security Studies Commons, Forensic Science and Technology Commons
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