MSDA-GDS: A Dual-Branch Hybrid Federated Explainable Deep Learning Framework for CAN Bus Intrusion Detection in Internet of Vehicles. - Shakkah, Moh; Al-sellami, Belal; A Hagar, Abdulnaser; Tawfik, Mohammed (Statistics, Optimization & Information Computing, 2026)
The Controller Area Network (CAN) bus remains critically vulnerable to cyberattacks due to its lack of authentication and encryption. Existing intrusion detection systems (IDS) for Internet of Vehicles (IoV) suffer from single-branch architectures that fail to capture multi-scale CAN byte dependencies, centralized training paradigms that compromise vehicular data privacy, and insufficient model interpretability. This paper proposes MSDA-GDS, a dualbranch hybrid federated explainable framework comprising a Multi-Scale Dilated Attention (MSDA) branch with parallel dilated convolutions and channel-spatial attention, and a Gated Depthwise Separable (GDS) branch with learnable gating mechanisms and residual connections, fused via learned attention weighting. The framework integrates Apache Spark-accelerated preprocessing, FedProx federated learning with differential privacy, and multi-method explainability (SHAP, LIME, gradient saliency). Evaluation on CICIoV2024 (1,408,219 CAN frames) and CIC-IDS-2017 (2.83M flows) demonstrates 99.99% and 99.40% accuracy respectively, with the federated variant achieving 99.97% under full privacy protection. Ablation analysis confirms the gating mechanism (∆F1 = −0.21) and engineered features (∆F1 = −0.27) as the most impactful components, while XAI analysis identifies DATA 2, DATA 1, and DATA 3 as the most discriminative byte positions with high cross-method consistency (ρ = 0.978).