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Quantifying the need for supervised machine learning in conducting liveforensic analysis of emergent configurations (ECO) in IoT environments
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0003-4071-4596
Edith Cowan University Australia.
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
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2020 (English)In: Forensic Science International: Reports, ISSN 2665-9107, Vol. 2, article id 100122Article in journal, Editorial material (Other academic) Published
Abstract [en]

Machine learning has been shown as a promising approach to mine larger datasets, such as those that comprise datafrom a broad range of Internet of Things devices, across complex environment(s) to solve different problems. Thispaper surveys existing literature on the potential of using supervised classical machine learning techniques, such asK-Nearest Neigbour, Support Vector Machines, Naive Bayes and Random Forest algorithms, in performing livedigital forensics for different IoT configurations. There are also a number of challenges associated with the use ofmachine learning techniques, as discussed in this paper.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 2, article id 100122
Keywords [en]
Supervised machine, Learning, Live forensics, Emergent configurations, IoT
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mau:diva-37145DOI: 10.1016/j.fsir.2020.100122Scopus ID: 2-s2.0-85099007368OAI: oai:DiVA.org:mau-37145DiVA, id: diva2:1506992
Available from: 2020-12-06 Created: 2020-12-06 Last updated: 2024-02-05Bibliographically approved

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Kebande, Victor R.Alawadi, Sadi

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