Efficient Graph-Based Intrusion Detection for CAN Bus Security with Minimal Training Data

In this study, we explore the efficacy of using a graph-based intrusion detection system (GBIDS) for Controller Area Network (CAN) bus security with minimal training data. Traditional machine learning models often require extensive data for training, typically around 70%, to achieve high accuracy and reliability. However, our approach demonstrates that even with significantly reduced training data—specifically, using just 30% of the available 70% of data (21% of total data)—we can maintain high levels of accuracy, precision, recall, and F1 score. This minimal data training is facilitated by on-the-fly training capabilities akin to edge computing, allowing for real-time adaptation and learning. Our results indicate that it is not always necessary to rely on the conventional 70% training data benchmark to ensure robust CAN bus security. By leveraging advanced graph-based algorithms and edge computing techniques, we achieve efficient and effective intrusion detection with reduced data requirements, thus offering a promising solution for real-time CAN bus security in resource-constrained environments.