AAFM-Net: An Ensemble CNN with Auxiliary Attention Filtering Module for Intrusion Detection

In recent years, electric vehicles (EV) have developed greatly and have begun to gradually replace traditional cars. With this development, the potential security threats faced by electric vehicle network systems are also increasing. To cope with these threats in the EV network, in the paper we propose an auxiliary attention filtering module (AAFM) that cooperates with an ensemble convolutional neural network (CNN). AAFM combines the attention of features from different dimensions, filters irrelevant features, and compensates for the output of the model. The latest CICEV2023 dataset is used for training and evaluation. Experiments demonstrate that AAFM can effectively improve model performance and deal with attacks in extreme situations compared to 8 classic and effective intrusion detection models. AAFM-Net achieves over 90% accuracy.