Position Verification in Connected Vehicles for Cyber Resilience Using Geofencing and Fuzzy Logic

Position verification is essential in connected and autonomous vehicle technology to enable secure vehicle-to-everything communication. Previous attempts to verify location information have used specific hardware, traffic parameters, and statistical model-based techniques dependent on neighbouring v...

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Main Authors: Maria Drolence Mwanje, Omprakash Kaiwartya, Abdallah Naser
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of Intelligent Transportation Systems
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10663467/
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author Maria Drolence Mwanje
Omprakash Kaiwartya
Abdallah Naser
author_facet Maria Drolence Mwanje
Omprakash Kaiwartya
Abdallah Naser
author_sort Maria Drolence Mwanje
collection DOAJ
description Position verification is essential in connected and autonomous vehicle technology to enable secure vehicle-to-everything communication. Previous attempts to verify location information have used specific hardware, traffic parameters, and statistical model-based techniques dependent on neighbouring vehicles and roadside infrastructure and whose judgements can be influenced by untrustworthy entities. Considering the back-and-forth communications during verification, these techniques are also unsuitable in the dynamic vehicular networking environment. In this context, this paper proposes a self-reliant trustbased position verification technique using dynamic geofencing, neural network, and Mamdani fuzzy logic controller. The method uses vehicular dynamics, such as distance between the sender and receiver vehicles, magnitude of the speed difference, and direction, to verify the trustworthiness of vehicle positions. An experimental analysis of a dataset of simulated driving scenarios in MATLAB demonstrates that the feedforward neural network records the highest direction classification performance at 99.8% in conjunction with the centroid defuzzification method. Subsequently, further quantitative analysis, including the Receiver Operating Characteristic curve with Area Under Curve and trust level distribution histograms, indicates that the suggested classification model outperforms a random classifier and effectively identifies false position data from the actual during trust computation.
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institution Kabale University
issn 2687-7813
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publishDate 2024-01-01
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series IEEE Open Journal of Intelligent Transportation Systems
spelling doaj-art-d081d856b31b49c68b1edd02bdd39adf2025-01-24T00:02:45ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-01554055410.1109/OJITS.2024.345366610663467Position Verification in Connected Vehicles for Cyber Resilience Using Geofencing and Fuzzy LogicMaria Drolence Mwanje0https://orcid.org/0000-0001-7996-9831Omprakash Kaiwartya1https://orcid.org/0000-0001-9669-8244Abdallah Naser2https://orcid.org/0000-0001-5969-1756Department of Computer Science, Nottingham Trent University, Nottingham, U.K.Department of Computer Science, Nottingham Trent University, Nottingham, U.K.Department of Computer Science, Nottingham Trent University, Nottingham, U.K.Position verification is essential in connected and autonomous vehicle technology to enable secure vehicle-to-everything communication. Previous attempts to verify location information have used specific hardware, traffic parameters, and statistical model-based techniques dependent on neighbouring vehicles and roadside infrastructure and whose judgements can be influenced by untrustworthy entities. Considering the back-and-forth communications during verification, these techniques are also unsuitable in the dynamic vehicular networking environment. In this context, this paper proposes a self-reliant trustbased position verification technique using dynamic geofencing, neural network, and Mamdani fuzzy logic controller. The method uses vehicular dynamics, such as distance between the sender and receiver vehicles, magnitude of the speed difference, and direction, to verify the trustworthiness of vehicle positions. An experimental analysis of a dataset of simulated driving scenarios in MATLAB demonstrates that the feedforward neural network records the highest direction classification performance at 99.8% in conjunction with the centroid defuzzification method. Subsequently, further quantitative analysis, including the Receiver Operating Characteristic curve with Area Under Curve and trust level distribution histograms, indicates that the suggested classification model outperforms a random classifier and effectively identifies false position data from the actual during trust computation.https://ieeexplore.ieee.org/document/10663467/Defuzzificationfuzzificationfuzzy logicgeofencinglocation verificationposition verification
spellingShingle Maria Drolence Mwanje
Omprakash Kaiwartya
Abdallah Naser
Position Verification in Connected Vehicles for Cyber Resilience Using Geofencing and Fuzzy Logic
IEEE Open Journal of Intelligent Transportation Systems
Defuzzification
fuzzification
fuzzy logic
geofencing
location verification
position verification
title Position Verification in Connected Vehicles for Cyber Resilience Using Geofencing and Fuzzy Logic
title_full Position Verification in Connected Vehicles for Cyber Resilience Using Geofencing and Fuzzy Logic
title_fullStr Position Verification in Connected Vehicles for Cyber Resilience Using Geofencing and Fuzzy Logic
title_full_unstemmed Position Verification in Connected Vehicles for Cyber Resilience Using Geofencing and Fuzzy Logic
title_short Position Verification in Connected Vehicles for Cyber Resilience Using Geofencing and Fuzzy Logic
title_sort position verification in connected vehicles for cyber resilience using geofencing and fuzzy logic
topic Defuzzification
fuzzification
fuzzy logic
geofencing
location verification
position verification
url https://ieeexplore.ieee.org/document/10663467/
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AT omprakashkaiwartya positionverificationinconnectedvehiclesforcyberresilienceusinggeofencingandfuzzylogic
AT abdallahnaser positionverificationinconnectedvehiclesforcyberresilienceusinggeofencingandfuzzylogic