EQAI: Explainable Quantum-Empowered Antispoofing Intelligence for Trustworthy Connected Autonomous Vehicles Communication

The increasing complexity of connected and autonomous vehicles (CAVs) introduces new security challenges in the Internet of Vehicles (IoV), where traditional detection models struggle with real-time interpretability, scalability, and robustness against spoofing attacks. This paper proposes EQAI—an Explainable Quantum Artificial Intelligence framework that fuses quantum-enhanced learning with interpretable trust inference for securing vehicular communications. The EQAI model employs an 8-qubit variational quantum circuit (VQC) integrated with lightweight classical layers and explainability modules based on SHAP and LIME. Using the CICIoV2024 dataset, the framework achieves a detection accuracy of 92.85%, a Class 3 F1-score of 82.1%, and a low false alarm rate (FAR ≤ 0.026), outperforming existing machine learning, blockchain-based, and federated learning approaches. Its compact design—with only 4,900 trainable parameters and ∼19.5k FLOPs—demonstrates real-time deployability and energy efficiency at the network edge. Moreover, LIME and SHAP analyses reveal transparent feature-level reasoning, enhancing operator trust and system explainability. The proposed EQAI architecture thus establishes a scalable, interpretable, and quantum-empowered foundation for secure, trustworthy, and intelligent vehicular networks, advancing toward resilient IoV and next-generation cybercognitive mobility ecosystems.