PlumX Metrics
Embed PlumX Metrics

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
  • 31
    Citations
  • 151
    Usage
  • 79
    Captures
  • 0
    Mentions
  • 52
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

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.

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know