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Malicious Node Detection in Vehicular Ad-Hoc Network Using Machine Learning and Deep Learning
Windsor University, Canada.
Windsor University, Canada.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Ken Saro-Wiwa Polytechnic, Bori, Nigeria.
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2019 (English)In: 2018 IEEE Globecom Workshops, GC Wkshps 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2019Conference paper, Published paper (Refereed)
Abstract [en]

Vehicular Ad hoc Networks (VANETs) provide effective vehicular operation for safety as well as greener and more efficient communication of vehicles in the Dedicated Short Range Communication (DRSC). The dynamic nature of the vehicular network topology has posed many security challenges for effective communication among vehicles. Consequently, models have been applied in the literature to checkmate the security issues in the vehicular networks. Existing models lack flexibility and sufficient functionality in capturing the dynamic behaviors of malicious nodes in the highly volatile vehicular communication systems. Given that existing models have failed to meet up with the challenges involved in vehicular network topology, it has become imperative to adopt complementary measures to tackle the security issues in the system. The approach of trust model with respect to Machine/Deep Learning (ML/DL) is proposed in the paper due to the gap in the area of network security by the existing models. The proposed model is to provide a data-driven approach in solving the security challenges in dynamic networks. This model goes beyond the existing works conceptually by modeling trust as a classification process and the extraction of relevant features using a hybrid model like Bayesian Neural Network that combines deep learning with probabilistic modeling for intelligent decision and effective generalization in trust computation of honest and dishonest nodes in the network. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019.
Keywords [en]
Dedicated short range communications, Deep learning, Knowledge based systems, Machine learning, Network security, Neural networks, Topology, Bayesian neural networks, Classification process, Effective communication, Efficient communications, Malicious node detections, Probabilistic modeling, Vehicular Adhoc Networks (VANETs), Vehicular communications, Vehicular ad hoc networks
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:mdh:diva-43071DOI: 10.1109/GLOCOMW.2018.8644127Scopus ID: 2-s2.0-85063434879ISBN: 9781538649206 (print)OAI: oai:DiVA.org:mdh-43071DiVA, id: diva2:1314651
Conference
2018 IEEE Globecom Workshops, GC Wkshps 2018, 9 December 2018 through 13 December 2018
Available from: 2019-05-09 Created: 2019-05-09 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
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  • Other locale
More languages
Output format
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  • asciidoc
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