A MULTI-AGENT REINFORCEMENT LEARNING BLOCKCHAIN FRAMEWORK FOR IMPROVING VEHICULAR INTERNET OF THINGS CYBERSECURITY

The Vehicular Internet of Things (VIoT) is a novel idea in the field of connected transportation systems that defines a new paradigm. Nevertheless, even the most modern and complex system will require additional and more powerful layers to protect the conversation from interception and the data from leakage. The centralized models have problems like trust problems and that there might be vulnerabilities and that is why there are attempts to integrate decentralization in operation. The first challenge in the VIoT networks is that the security and openness of such a connection and data transfer are still not well developed. Another issue is the security and dependability of the interaction between the vehicles and the infrastructure, although this issue is magnified by the size of the VIoT network. This research is a blockchain and game theory base research that uses Multi-Agent Reinforcement Learning (MARL) to improve the security and efficiency of the VIoT ecosystem. The technology of blockchain gives a distributed ledger where data cannot be altered or erased. Moreover, the MARL architecture allows for the realisation of better decisions for each of the members of the network. To this, the set that was made up of the smart contracts, Vehicle Units (VUs) and the decentralized servers that form the proposed architecture would be added to allow for the right flow and processing of the data. The blockchain's decentralized nature provides a guarantee for all secure, immutable data transfers and transparent transactions throughout the network of the VIoT. MARL enables agents to learn and acquire the best strategies as they pass through time, which leads to secure and effective communication among entities. Besides, the implementation of lightweight cryptography techniques and strategic selections according to game theory help to protect and improve the performance of the security system of the VIoT ecosystem.