An Optimized Graph Neural Network-Based Approach for Intrusion Detection in Smart Vehicles

Due to recent developments in vehicle networks (VNs), more and more people want their electric cars to have access to sophisticated networking features and perform a variety of advanced activities. Several technologies are installed in these autonomous vehicles to aid the drivers. Cars have unquestionably become smarter as we’ve been able to install more and more gadgets and applications on them. As a result, the security of assistant and self-drive systems in automobiles becomes a life-threatening concern, since hostile attacks that cause traffic accidents may infiltrate smart cars. This research provides a technique based on the Graph Neural Network (GNN) deep learning (DL) model for detecting intrusions in VNs. This model can recognize attack patterns and classify threats accordingly. Experimentation utilizes car-hacking datasets. These files include DoS attacks, fuzzy attacks, driving gear spoofing, and RPM gauge spoofing. The car-hacking dataset is converted into image files, and the GNN model provided works with the newly produced image dataset. The findings of comparing our model to DL models indicate that our GNN model is superior. In specific, the test case scenarios may identify abnormalities with respect to detection F1-score, recall, precision, and accuracy to ensure accurate detection and identification of potential false alarm concerns.