In order to improve the accuracy of detecting the cyber-attacks in Internet of vehicles,hyper-parameter op-timization 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 par-ticle swarm optimization was utilized to optimize the hyber-parameters of the CNN,and then CNN model was opti-mized.Confidence averaging and concatenation techniques were constructed to improve the accuracy.The perfor-mance of the proposed CNES was measured based on Car-Hacking and CICIDS2017 datasets.This shows the effec-tiveness of the proposed CNES for cyber-attack detection.The CNES achieves F1 score of 100%on Car-Hacking dataset.
Internet of vehiclesintrusion detectionconvolution neural networkparticle swarm optimization algo-rithmensemble learning
NETL
NSTL
万方数据
