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Malicious Node Detection in Vehicular Ad-Hoc Network Using Machine Learning and Deep Learning
Windsor Univ, Windsor, ON, Canada..
Windsor Univ, Windsor, ON, Canada..
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. RISE SICS, Vasteras, Sweden..
Ken Saro Wiwa Polytech, Bori, Nigeria..
Show others and affiliations
2018 (English)In: 2018 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), IEEE , 2018Conference 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
IEEE , 2018.
Series
IEEE Globecom Workshops, ISSN 2166-0069
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-43148ISI: 000462817000040ISBN: 978-1-5386-4920-6 (print)OAI: oai:DiVA.org:mdh-43148DiVA, id: diva2:1305833
Conference
IEEE Global Telecommunications Conference (GC Wkshps), DEC 09-13, 2018, Abu Dhabi, U ARAB EMIRATES
Available from: 2019-04-18 Created: 2019-04-18 Last updated: 2019-06-11Bibliographically approved

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Balador, Ali

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf