Quantifying the need for supervised machine learning in conducting live forensic analysis of emergent configurations (ECO) in IoT environments
Forensic Science International: Reports, ISSN: 2665-9107, Vol: 2, Page: 100122
2020
- 31Citations
- 151Usage
- 79Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations31
- Citation Indexes31
- 31
- CrossRef25
- Usage151
- Downloads121
- Abstract Views30
- Captures79
- Readers79
- 79
Article Description
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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2665910720300712; http://dx.doi.org/10.1016/j.fsir.2020.100122; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85099007368&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2665910720300712; https://ro.ecu.edu.au/ecuworkspost2013/9600; https://ro.ecu.edu.au/cgi/viewcontent.cgi?article=10606&context=ecuworkspost2013; https://dx.doi.org/10.1016/j.fsir.2020.100122
Elsevier BV
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