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A fuzzy-based context-aware misbehavior detecting scheme for detecting rogue nodes in vehicular ad hoc network

A vehicular ad hoc network (VANET) is an emerging technology that improves road safety, traffic efficiency, and passenger comfort. VANETs’ applications rely on co-operativeness among vehicles by periodically sharing their context information, such as position speed and acceleration, among others, at...

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Main Authors: Abdoh Ghaleb, Fuad Abdulgaleel, Saeed, Faisal, Alkhammash, Eman H., Alghamdi, Norah Saleh, Al-rimy, Bander Ali Saleh
Format: Article
Language:English
Published: MDPI 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/103999/1/FuadAbdulgaleelAbdoh2022_AFuzzyBasedContextAwareMisbehavior.pdf
http://eprints.utm.my/103999/
http://dx.doi.org/10.3390/s22072810
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spelling my.utm.1039992024-01-09T00:50:41Z http://eprints.utm.my/103999/ A fuzzy-based context-aware misbehavior detecting scheme for detecting rogue nodes in vehicular ad hoc network Abdoh Ghaleb, Fuad Abdulgaleel Saeed, Faisal Alkhammash, Eman H. Alghamdi, Norah Saleh Al-rimy, Bander Ali Saleh QA75 Electronic computers. Computer science A vehicular ad hoc network (VANET) is an emerging technology that improves road safety, traffic efficiency, and passenger comfort. VANETs’ applications rely on co-operativeness among vehicles by periodically sharing their context information, such as position speed and acceleration, among others, at a high rate due to high vehicles mobility. However, rogue nodes, which exploit the co-operativeness feature and share false messages, can disrupt the fundamental operations of any potential application and cause the loss of people’s lives and properties. Unfortunately, most of the current solutions cannot effectively detect rogue nodes due to the continuous context change and the inconsideration of dynamic data uncertainty during the identification. Although there are few context-aware solutions proposed for VANET, most of these solutions are data-centric. A vehicle is considered malicious if it shares false or inaccurate messages. Such a rule is fuzzy and not consistently accurate due to the dynamic uncertainty of the vehicular context, which leads to a poor detection rate. To this end, this study proposed a fuzzy-based context-aware detection model to improve the overall detection performance. A fuzzy inference system is constructed to evaluate the vehicles based on their generated information. The output of the proposed fuzzy inference system is used to build a dynamic context reference based on the proposed fuzzy inference system. Vehicles are classified into either honest or rogue nodes based on the deviation of their evaluation scores calculated using the proposed fuzzy inference system from the context reference. Extensive experiments were carried out to evaluate the proposed model. Results show that the proposed model outperforms the state-of-the-art models. It achieves a 7.88% improvement in the overall performance, while a 16.46% improvement is attained for detection rate compared to the state-of-the-art model. The proposed model can be used to evict the rogue nodes, and thus improve the safety and traffic efficiency of crewed or uncrewed vehicles designed for different environments, land, naval, or air. MDPI 2022-04-01 Article PeerReviewed application/pdf en http://eprints.utm.my/103999/1/FuadAbdulgaleelAbdoh2022_AFuzzyBasedContextAwareMisbehavior.pdf Abdoh Ghaleb, Fuad Abdulgaleel and Saeed, Faisal and Alkhammash, Eman H. and Alghamdi, Norah Saleh and Al-rimy, Bander Ali Saleh (2022) A fuzzy-based context-aware misbehavior detecting scheme for detecting rogue nodes in vehicular ad hoc network. Sensors, 22 (7). pp. 1-21. ISSN 1424-8220 http://dx.doi.org/10.3390/s22072810 DOI:10.3390/s22072810
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Abdoh Ghaleb, Fuad Abdulgaleel
Saeed, Faisal
Alkhammash, Eman H.
Alghamdi, Norah Saleh
Al-rimy, Bander Ali Saleh
A fuzzy-based context-aware misbehavior detecting scheme for detecting rogue nodes in vehicular ad hoc network
description A vehicular ad hoc network (VANET) is an emerging technology that improves road safety, traffic efficiency, and passenger comfort. VANETs’ applications rely on co-operativeness among vehicles by periodically sharing their context information, such as position speed and acceleration, among others, at a high rate due to high vehicles mobility. However, rogue nodes, which exploit the co-operativeness feature and share false messages, can disrupt the fundamental operations of any potential application and cause the loss of people’s lives and properties. Unfortunately, most of the current solutions cannot effectively detect rogue nodes due to the continuous context change and the inconsideration of dynamic data uncertainty during the identification. Although there are few context-aware solutions proposed for VANET, most of these solutions are data-centric. A vehicle is considered malicious if it shares false or inaccurate messages. Such a rule is fuzzy and not consistently accurate due to the dynamic uncertainty of the vehicular context, which leads to a poor detection rate. To this end, this study proposed a fuzzy-based context-aware detection model to improve the overall detection performance. A fuzzy inference system is constructed to evaluate the vehicles based on their generated information. The output of the proposed fuzzy inference system is used to build a dynamic context reference based on the proposed fuzzy inference system. Vehicles are classified into either honest or rogue nodes based on the deviation of their evaluation scores calculated using the proposed fuzzy inference system from the context reference. Extensive experiments were carried out to evaluate the proposed model. Results show that the proposed model outperforms the state-of-the-art models. It achieves a 7.88% improvement in the overall performance, while a 16.46% improvement is attained for detection rate compared to the state-of-the-art model. The proposed model can be used to evict the rogue nodes, and thus improve the safety and traffic efficiency of crewed or uncrewed vehicles designed for different environments, land, naval, or air.
format Article
author Abdoh Ghaleb, Fuad Abdulgaleel
Saeed, Faisal
Alkhammash, Eman H.
Alghamdi, Norah Saleh
Al-rimy, Bander Ali Saleh
author_facet Abdoh Ghaleb, Fuad Abdulgaleel
Saeed, Faisal
Alkhammash, Eman H.
Alghamdi, Norah Saleh
Al-rimy, Bander Ali Saleh
author_sort Abdoh Ghaleb, Fuad Abdulgaleel
title A fuzzy-based context-aware misbehavior detecting scheme for detecting rogue nodes in vehicular ad hoc network
title_short A fuzzy-based context-aware misbehavior detecting scheme for detecting rogue nodes in vehicular ad hoc network
title_full A fuzzy-based context-aware misbehavior detecting scheme for detecting rogue nodes in vehicular ad hoc network
title_fullStr A fuzzy-based context-aware misbehavior detecting scheme for detecting rogue nodes in vehicular ad hoc network
title_full_unstemmed A fuzzy-based context-aware misbehavior detecting scheme for detecting rogue nodes in vehicular ad hoc network
title_sort fuzzy-based context-aware misbehavior detecting scheme for detecting rogue nodes in vehicular ad hoc network
publisher MDPI
publishDate 2022
url http://eprints.utm.my/103999/1/FuadAbdulgaleelAbdoh2022_AFuzzyBasedContextAwareMisbehavior.pdf
http://eprints.utm.my/103999/
http://dx.doi.org/10.3390/s22072810
_version_ 1789424363697602560
score 13.211869

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