Explainable artificial intelligence for intrusion detection in connected vehicles

As Connected Vehicles (CVs) increasingly depend on deep learning-based Intrusion Detection Systems (IDS), the need for models that are both accurate and interpretable has become essential. This paper explores the use of Explainable Artificial Intelligence (XAI) techniques to improve the transparency of a Convolutional Neural Network (CNN)- based IDS trained on the CICIoV2024 dataset. We evaluate four widely adopted XAI methods—SHAP, LIME, Integrated Gradients, and Grad- CAM—by examining their ability to explain predictions across various cyberattack scenarios, including spoofing and denial-of-service (DoS) at- tacks on CAN bus traffic. Our results show that SHAP and Integrated Gradients effectively highlight key features, with SHAP assigning up to
0.16 contribution to specific class decisions. LIME provided near-perfect agreement with the model’s predictions in local explanations, while Grad- CAM offered visual insights aligned with convolutional activations. The CNN model achieved 98.3% classification accuracy on the CICIoV2024 test set. These findings offer practical recommendations for selecting XAI tools in automotive cybersecurity and contribute to building trustworthy, explainable IDS for intelligent transportation systems.