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Machine learning-based recommendation trust model for machine-to-machine communication
Windsor University, Canada.
Universidad de Pamplona, Colombia.
Federal University of Technology, Mina, Nigeria.
Ken Saro-Wiwa Polytechnic, Bori, Nigeria.
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2019 (English)In: 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018, Institute of Electrical and Electronics Engineers Inc. , 2019Conference paper, Published paper (Refereed)
Abstract [en]

The Machine Type Communication Devices (MTCDs) are usually based on Internet Protocol (IP), which can cause billions of connected objects to be part of the Internet. The enormous amount of data coming from these devices are quite heterogeneous in nature, which can lead to security issues, such as injection attacks, ballot stuffing, and bad mouthing. Consequently, this work considers machine learning trust evaluation as an effective and accurate option for solving the issues associate with security threats. In this paper, a comparative analysis is carried out with five different machine learning approaches: Naive Bayes (NB), Decision Tree (DT), Linear and Radial Support Vector Machine (SVM), KNearest Neighbor (KNN), and Random Forest (RF). As a critical element of the research, the recommendations consider different Machine-to-Machine (M2M) communication nodes with regard to their ability to identify malicious and honest information. To validate the performances of these models, two trust computation measures were used: Receiver Operating Characteristics (ROCs), Precision and Recall. The malicious data was formulated in Matlab. A scenario was created where 50% of the information were modified to be malicious. The malicious nodes were varied in the ranges of 10%, 20%, 30%, 40%, and the results were carefully analyzed.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019.
Keywords [en]
Internet of Things(IoTs), Internet of Vehicles(IoVs), Machine Type Communication Devices, Machine-to-Machine(M2M), Supervisory Control and Data Supervisory Acquisition(SCADA), Decision trees, Internet protocols, Learning algorithms, Machine learning, Nearest neighbor search, Network security, Signal processing, Support vector machines, Vehicle to vehicle communications, Comparative analysis, Internet of thing (IoTs), K nearest neighbor (KNN), Machine learning approaches, Machine type communications, Machine-to-machine (M2M), Receiver operating characteristics, Supervisory control, Machine-to-machine communication
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-43502DOI: 10.1109/ISSPIT.2018.8705147Scopus ID: 2-s2.0-85065641118ISBN: 9781538675687 (print)OAI: oai:DiVA.org:mdh-43502DiVA, id: diva2:1318711
Conference
2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018, 6 December 2018 through 8 December 2018
Available from: 2019-05-28 Created: 2019-05-28 Last updated: 2019-06-11Bibliographically approved

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Balador, Ali

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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Language
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
  • rtf