Asynchronous Federated Learning for Intrusion Detection in Vehicular Cyber-Physical Systems

In recent years, development in IoV technologies has reached more promising progress. IoV technology helps vehicles interact and exchange information between public networks and the surrounding environment, which reduces road congestion. In order to protect the information from attack and to provide efficient data transmission, this paper proposes secure federated learning for a vehicular cyber-physical system using an Inter-polated public key and private key-ROTation (IPP-ROT)-based Elliptic Curve Cryptography (ECC) and Fed Buff: Federated Learning with Buffered Asynchronous Aggregation based Log Sigmoid Multi-Layer Perceptron (FB-FL-BAA-LSMLP) techniques. Initially, the vehicles are registered with a cloud server by generating keys and cipher text using ECC and IPP-ROT algorithms. After that, vehicle parameters are sensed by the server. As a large number of vehicles cross the Road Side Units (RSU), hashing is performed to authenticate the vehicle crossing RSUs using the Digit Folding-based Hash of Variable Length (DF-HAVAL) algorithm to avoid data collisions and uneven delays. Further, the data classification performed using FB-FL-BAA-LSMLP, which classifies data, and attacked data will be detected. At last, the performance of the proposed method is verified by comparing it with the existing techniques, and the results show better performance than the other methods.