Integration of Quantum-Inspired Evolutionary Algorithms for Enhanced Mitigation of Complex Gray Hole Attacks in VANETs
Keywords:
Vehicular Ad-Hoc Networks (VANETs), Gray Hole Attacks, Quantum-Inspired Optimization, Blockchain-Assisted Trust Management, Anomaly Detection, Intelligent Transportation Systems (ITS)Abstract
Vehicular Ad-Hoc Networks (VANETs) play a pivotal role in Intelligent Transportation Systems (ITS), facilitating real-time communication among vehicles and roadside units to enhance traffic management and road safety. However, VANETs are susceptible to gray hole attacks, where malicious nodes selectively drop packets, undermining network reliability. This study proposes a novel hybrid methodology integrating Quantum-Inspired Particle Swarm Optimization (QPSO) and Blockchain-Assisted Trust Mechanism (BATM) to detect and mitigate gray hole attacks effectively. QPSO optimizes routing by dynamically identifying secure and high-throughput paths, while BATM ensures decentralized, tamperproof trust management. Simulations conducted using OMNeT++ and SUMO demonstrate significant improvements over existing approaches. The proposed method achieved a Packet Delivery Ratio (PDR) of 94.8%, surpassing the benchmarks set by Abdul Malik et al. (90.3%) and Rini & Meena (91.2%). Throughput increased to 238.7 kbps, compared to 230.0 kbps and 225.3 kbps reported in recent studies. Furthermore, the end-to-end delay was reduced to 112.4 ms , significantly lower than 120.3 ms and 118.7 ms achieved by previous methods. Packet loss was minimized to 4.5%, and the trust detection accuracy reached 96.8%, indicating superior identification of malicious nodes. This study also highlights the scalability and energy efficiency of the proposed solution, which remained robust even in high-node-density scenarios. The integration of QPSO and BATM offers a practical, low overhead solution for mitigating gray hole attacks, paving the way for secure and efficient VANET operations in dynamic environments.
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