IoV-BCFL: An intrusion detection method for IoV based on blockchain and federated learning

Abstract

In recent years, Internet of Vehicles (IoV) is in a booming stage. But at the same time, the methods of attack against IoV such as Denial of Service (DoS) and deception are great threats to personal and social security. Traditional IoV intrusion detection usually adopts a centralized detection model, which has the disadvantages of untimely detection results and is difficult to protect vehicle privacy in practical applications. Meanwhile, centralized computation requires a large amount of vehicle data transmission, which overloads the wireless bandwidth. Combined the distributed computing resources of Federated Learning (FL) and the decentralized features of blockchain, an IoV intrusion detection framework named IoV-BCFL is proposed, which is capable of distributed intrusion detection and reliable logging with privacy protection. FL is used for distributing training on vehicle nodes and aggregating the training models at Road Side Unit (RSU) to reduce data transmission, protect the privacy of training data, and ensure the security of the model. A blockchain-based intrusion logging mechanism is presented, which enhances vehicle privacy protection through Rivest-Shamir-Adleman (RSA) algorithm encryption and uses Inter Planetary File System (IPFS) to store the intrusion logs. The intrusion behavior can be faithfully recorded by logging smart contract after detecting the intrusion, which can be used to track intruders, analyze security vulnerabilities, and collect evidence. Experiments based on different open source datasets show that FL achieves a high detection rates on intrusion data and effectively reduce the communication overhead, the smart contract performs well on evaluation indicators such as sending rate, latency, and throughput.