Enhancing network intrusion detection of the Internet of Vehicles: Challenges and proposed solutions

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University of New Brunswick


While connected vehicles improve driving experience through the Internet of Vehicles network, this connectivity brings privacy and security risks. Current Machine Learning based Intrusion detection Systems (IDS) encounter challenges like preserving users’ privacy, and interpretability. Current intra-vehicle IDS rely on central servers for data aggregation and training, which consumes bandwidth, and jeopardizes user privacy. We introduce ImageFed, an intra-vehicle IDS that employs federated learning with a Convolutional Neural Network to enable distributed learning while preserving users’ privacy. Evaluations show an average F1-score of 99.54% and 99.87% accuracy on CAN-Intrusion dataset with low detection latency. Inter-vehicle networks demand deep learning frameworks for better generalization over intricate attacks. However, Deep Neural Networks (DNN) lack interpretability, eroding trust among experts. To address this, we introduce a rule extraction framework for DNN-based IDS, enhancing transparency via interpretable rule trees. The framework achieves 94% accuracy and 88% F1-score on CICIDS2017 dataset.