An intrusion detection model based on convolution neural network for Internet of vehicles - ZHANG Rui (Dianxin kexue)
In order to improve the accuracy of detecting the cyber-attacks in Internet of vehicles, hyper-parameter optimization convolution neural network-based ensemble Intrusion detection system (CNES) was proposed. In CNES, the convolution neural network (CNN) was adopted to serve as based learner in ensemble learning. Moreover, the particle swarm optimization was utilized to optimize the hyber-parameters of the CNN, and then CNN model was optimized. Confidence averaging and concatenation techniques were constructed to improve the accuracy. The performance of the proposed CNES was measured based on Car-Hacking and CICIDS2017 datasets. This shows the effectiveness of the proposed CNES for cyber-attack detection. The CNES achieves F1 score of 100% on Car-Hacking dataset.