Softwar-defined vehicle network security using blockchain approach

Software-Defined Vehicular Networks (SDVNs) are important in facilitating intelligent transport systems since vehicles communicate with infrastructure, cloud services, and other vehicles. Nonetheless, the very dynamic character of vehicular settings places these networks at high risks of security risks such as data manipulation, illegitimate access, and bad conduct of nodes. To overcome such difficulties, the proposed research suggests a blockchain-enabled adaptive security model to SDVNs that combines the multi-layer blockchain architecture with smart security solutions. The suggested model presents the Adaptive Consensus Selection Algorithm (ACSA) that will dynamically choose the most appropriate blockchain consensus protocol according to the current real-time network statistics like node density, transaction rate, and latency. The performance metrics that are used to test the framework on the performance of the vehicular network through simulation environments are the latency, throughput, scalability and security strength. The experimental findings show that the suggested adaptive blockchain architecture will considerably enhance the performance of a network by decreasing the latency and resource usage and improving the levels of transaction throughput and attack detection. The suggested solution will present an intelligent vehicle communication system of the next generation that is scalable and secure. The new mechanism is a dynamic consensus mechanism, which can be used to choose the most optimal blockchain protocol under the conditions of real-time SDVN network, enhancing latency and efficiency of transactions. The mathematical model that is to be developed to ensure secure SDVN Blockchain will be as follows:•A formal model is applied to optimize the cost of executing smart contracts without compromising the necessary amount of network security and performance.•Multi-layer blockchain security architecture ensures strong security and end-to-end protection and implements blockchain technology in networked environments.•An anomaly detection mechanism that is based on machine learning is implemented at the edge layer to identify malicious vehicular nodes and increase the network security, in general.