Design of an Improved Model for IoV Security Using Bioinspired Optimization & Scalable Blockchains

Urgent to the rising threat of attacks, such as Sybil, masquerading, and flooding attacks, whereby the integrity of the network and its performance become compromised severely, is the need for improved security and communication efficiency in IoV. This work would then propose a holistic approach integrating Blockchain-enhanced SDN and QoS slicing, bioinspired routing, machine learning-driven intrusion detection, fuzzy logic-based trust management, and deep reinforcement learning to address all these challenges in an integrated way. This proposed model utilizes Blockchain-based Secure Slicing with Smart Contracts to enhance authentication and resource management and provide a reduction of up to 30% latency along with 98% accuracy in preventing attacks. The application of ACO for routing enhancement reduces packet loss up to 10% in flooding attacks. A hybrid Intrusion Detection System integrating both machine learning and blockchain, with accuracy at 99.2% for Sybil, masquerading, and flooding attacks; Fuzzy logic-based trust management re-validates the routing by checking the trust scores at 95%; and the DRL-based mitigation system that dynamically adapts to new threats provides reduction in network downtime up to 35-40%. This multi-layered approach enhances security and performance for communication with which the IoV networks are to be resilient against evolving threats.