Quantum Federated Learning for DoS Attack Detection and Privacy Preserving of VANET : A Novel Hybrid Machine Learning Approach

ABSTRACT The integration of Vehicular Ad Hoc Networks (VANETs) has changed intelligent transportation systems by making it possible for vehicles, roadside units (RSUs), and traffic management infrastructure to talk to each other in real time. This feature makes the roads safer, more convenient for drivers, and more efficient for traffic, but it also makes VANET ecosystems vulnerable to many types of cyberattacks, including Denial‐of‐Service (DoS) and false data injection, which can be very dangerous for safety and privacy. Conventional security solutions frequently struggle to address the highly dynamic, decentralized, and latency‐sensitive characteristics of VANET environments. Intrusion Detection Systems (IDS) powered by Artificial Intelligence (AI) have become promising solutions, but there are still issues with computational overhead, secure model updates, and data privacy in distributed vehicular networks. To address these challenges, we introduce Quantum Lightweight Federated Learning (FL), an innovative hybrid machine learning framework that integrates the exponential computational power of Quantum Computing (QC) with the decentralized, privacy‐preserving advantages of FL. The suggested method combines knowledge distillation with the FL process to create a lightweight detection model that works well on vehicle nodes with limited resources. Moreover, QKD Encryption is used to protect model parameters during federated aggregation, making sure that end‐to‐end privacy is maintained without slowing down processing. Lastly, SHAP, an Explainable AI method, is used to make sense of the choices made by the proposed model. Using the CICDDoS‐2019 dataset for experimental validation shows that the proposed model is strong, with an accuracy of 99.36%, a high recall rate of 99.53%, and a precision rate of 99.38% across different attack scenarios.