SecureTrust-PI: A Privacy-Integrity Trust Management Model for Vehicular Edge-Cloud
Corresponding author: Shilpa
Abstract
Vehicular Ad-Hoc Networks (VANETs) integrated with Vehicular Edge-Cloud (VEC) frameworks enable seamless Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, enhancing both traffic safety and efficiency. However, VANET-enabled VEC environments are highly vulnerable to cyber threats, including unauthorized access, data tampering, and identity spoofing. Meanwhile, existing security solutions primarily focus on authentication and privacy, often neglecting data integrity, which leaves the system susceptible to manipulation attacks. To address this limitation, we propose the SecureTrust-PI model, which incorporates attack detection and misclassification minimization while optimizing network performance through efficient trust evaluation and integrity-preserving mechanisms. The model's performance was validated on an Internet of Vehicles (IoV) attack dataset under six adversarial scenarios ranging from 5% to 30% attack intensity and was compared against Practical Byzantine Fault Tolerance (PBFT). Results demonstrate that SecureTrust-PI consistently outperforms PBFT, achieving improvements of 26.16% in attack detection, 25.96% in misclassification reduction, 14.58% in throughput, 23.63% in delay, and 35.08% in energy efficiency.
Keywords:
attack detection, integrity, privacy, security, trust model, Vehicular Ad-Hoc Networks (VANETs), vehicular edge cloudDownloads
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