Collaborative detection of black hole and gray hole attacks for secure data communication in VANETs

Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerab...

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Main Authors: Shamim Younas, Faisal Rehman, Tahir Maqsood, Saad Mustafa, Adnan Akhunzada, Abdullah Gani
Format: Article
Language:English
English
Published: Multidisciplinary Digital Publishing Institute 2022
Subjects:
Online Access:https://eprints.ums.edu.my/id/eprint/36423/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/36423/2/FULLTEXT.pdf
https://eprints.ums.edu.my/id/eprint/36423/
https://doi.org/10.3390/app122312448
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spelling my.ums.eprints.364232023-08-17T04:00:32Z https://eprints.ums.edu.my/id/eprint/36423/ Collaborative detection of black hole and gray hole attacks for secure data communication in VANETs Shamim Younas Faisal Rehman Tahir Maqsood Saad Mustafa Adnan Akhunzada Abdullah Gani TE210-228.3 Construction details Including foundations, maintenance, equipment Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerable to a variety of attacks, including Black Hole, Gray Hole, wormhole, and rush attacks. These attacks are aimed at disrupting traffic between cars and on the roadside. The discovery of Black Hole attack has become an increasingly critical problem due to widespread adoption of autonomous and connected vehicles (ACVs). Due to the critical nature of ACVs, delay or failure of even a single packet can have disastrous effects, leading to accidents. In this work, we present a neural network-based technique for detection and prevention of rushed Black and Gray Hole attacks in vehicular networks. The work also studies novel systematic reactions protecting the vehicle against dangerous behavior. Experimental results show a superior detection rate of the proposed system in comparison with state-of-the-art techniques. Multidisciplinary Digital Publishing Institute 2022-12-05 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/36423/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/36423/2/FULLTEXT.pdf Shamim Younas and Faisal Rehman and Tahir Maqsood and Saad Mustafa and Adnan Akhunzada and Abdullah Gani (2022) Collaborative detection of black hole and gray hole attacks for secure data communication in VANETs. Applied Sciences, 12. pp. 1-17. https://doi.org/10.3390/app122312448
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic TE210-228.3 Construction details Including foundations, maintenance, equipment
spellingShingle TE210-228.3 Construction details Including foundations, maintenance, equipment
Shamim Younas
Faisal Rehman
Tahir Maqsood
Saad Mustafa
Adnan Akhunzada
Abdullah Gani
Collaborative detection of black hole and gray hole attacks for secure data communication in VANETs
description Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerable to a variety of attacks, including Black Hole, Gray Hole, wormhole, and rush attacks. These attacks are aimed at disrupting traffic between cars and on the roadside. The discovery of Black Hole attack has become an increasingly critical problem due to widespread adoption of autonomous and connected vehicles (ACVs). Due to the critical nature of ACVs, delay or failure of even a single packet can have disastrous effects, leading to accidents. In this work, we present a neural network-based technique for detection and prevention of rushed Black and Gray Hole attacks in vehicular networks. The work also studies novel systematic reactions protecting the vehicle against dangerous behavior. Experimental results show a superior detection rate of the proposed system in comparison with state-of-the-art techniques.
format Article
author Shamim Younas
Faisal Rehman
Tahir Maqsood
Saad Mustafa
Adnan Akhunzada
Abdullah Gani
author_facet Shamim Younas
Faisal Rehman
Tahir Maqsood
Saad Mustafa
Adnan Akhunzada
Abdullah Gani
author_sort Shamim Younas
title Collaborative detection of black hole and gray hole attacks for secure data communication in VANETs
title_short Collaborative detection of black hole and gray hole attacks for secure data communication in VANETs
title_full Collaborative detection of black hole and gray hole attacks for secure data communication in VANETs
title_fullStr Collaborative detection of black hole and gray hole attacks for secure data communication in VANETs
title_full_unstemmed Collaborative detection of black hole and gray hole attacks for secure data communication in VANETs
title_sort collaborative detection of black hole and gray hole attacks for secure data communication in vanets
publisher Multidisciplinary Digital Publishing Institute
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/36423/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/36423/2/FULLTEXT.pdf
https://eprints.ums.edu.my/id/eprint/36423/
https://doi.org/10.3390/app122312448
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score 13.18916