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: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10663467/ |
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| Summary: | 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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| ISSN: | 2687-7813 |