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• W3043488929 abstract "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." @default.
• W3043488929 created "2020-07-23" @default.
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• W3043488929 date "2020-12-01" @default.
• W3043488929 modified "2023-10-16" @default.
• W3043488929 title "Quantifying the need for supervised machine learning in conducting live forensic analysis of emergent configurations (ECO) in IoT environments" @default.
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• W3043488929 doi "https://doi.org/10.1016/j.fsir.2020.100122" @default.
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