The Controller Area Network (CAN) bus plays an essential role in Connected Autonomous Vehicles (CAVs), yet its inherent design limitations regarding data protection make it susceptible to malicious intrusions. Contemporary research in intrusion detection predominantly employs Long Short-Term Memory (LSTM) models to analyze CAN IDs as time series data. However, the high computational complexity of LSTM models makes them unsuitable for resource constrained in-vehicle network. To address this problem, a lightweight IDS combining image encoding and an Efficient Channel Attention (ECA) network is p...
