Quantifying the need for supervised machine learning in conducting liveforensic analysis of emergent configurations (ECO) in IoT environmentsShow others and affiliations
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
2020-12-062020-12-062024-06-17Bibliographically approved