Design and Validation of a Secure and Context-Aware Vehicular Ad Hoc Network (VANET) Framework Using Trust-Based Multiobjective Analysis

Abstract

– Vehicular Ad Hoc Networks (VANETs) support safety-critical vehicle-to-vehicle and vehicle-to-infrastructure communication for connected transportation, but their rapidly changing topology, intermittent connectivity, decentralized data ownership, heterogeneous traffic, and persistent cyberattacks make real-time security difficult. Existing approaches commonly address trust management, intrusion detection, key exchange, application-level quality of service, and event integrity as separate functions, which can produce fragmented security decisions, unnecessary communication overhead, weak context sensitivity, and limited end-to-end validation. This study proposes a Quantum Resilient VANET Architecture (QRVA) organized into two coupled functional layers. The first, a trust-learning-cryptographic security layer, integrates Context-Aware Probabilistic Trust Graph Modeling (CAPT-GM), the Federated Learning-Based VANET Security Framework (FLVASF), and the Quantum-Resilient Key Exchange Simulation Layer (QKX-SL). It dynamically estimates vehicle trust, excludes low-trust participants from federated IDS aggregation, and establishes quantum-resistant session keys only for validated peers. The second, a contextual performance and integrity-validation layer, integrates Contextual Multi-Metric Simulation Slicing (CM2S2) and the Hybrid Blockchain and Edge Evaluation Engine (HBEEE) to evaluate application-specific latency, throughput, and packet-delivery behavior while recording validated events through edge-assisted Merkle aggregation and lightweight consensus. Cross-layer feedback returns QoS and integrity evidence to the trust and learning processes, thereby linking detection, communication protection, performance analysis, and tamper-resistant validation in one decision loop. Evaluation using Veins, SUMO, OMNeT++, and the VeReMi misbehavior dataset produced 93.2-94.8% malicious-node detection accuracy, 95.9-96.4% federated attack-detection accuracy, 1.5-1.8% false-trust propagation, 2.1 ms lattice key-establishment latency, 17.2 MB communication overhead per 1000 vehicles per hour, and composite performance scores of 0.91-0.93.