An intrusion detection model based on convolution neural network for Internet of vehicles
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 ensem...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2024-12-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024243/ |
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| author | ZHANG Rui |
| author_facet | ZHANG Rui |
| author_sort | ZHANG Rui |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fa5b1807a1474cb89ebd8b43837781ad |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-fa5b1807a1474cb89ebd8b43837781ad2025-01-15T03:34:19ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-12-0140516279426087An intrusion detection model based on convolution neural network for Internet of vehiclesZHANG RuiIn 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024243/Internet of vehiclesintrusion detectionconvolution neural networkparticle swarm optimization algorithmensemble learning |
| spellingShingle | ZHANG Rui An intrusion detection model based on convolution neural network for Internet of vehicles Dianxin kexue Internet of vehicles intrusion detection convolution neural network particle swarm optimization algorithm ensemble learning |
| title | An intrusion detection model based on convolution neural network for Internet of vehicles |
| title_full | An intrusion detection model based on convolution neural network for Internet of vehicles |
| title_fullStr | An intrusion detection model based on convolution neural network for Internet of vehicles |
| title_full_unstemmed | An intrusion detection model based on convolution neural network for Internet of vehicles |
| title_short | An intrusion detection model based on convolution neural network for Internet of vehicles |
| title_sort | intrusion detection model based on convolution neural network for internet of vehicles |
| topic | Internet of vehicles intrusion detection convolution neural network particle swarm optimization algorithm ensemble learning |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024243/ |
| work_keys_str_mv | AT zhangrui anintrusiondetectionmodelbasedonconvolutionneuralnetworkforinternetofvehicles AT zhangrui intrusiondetectionmodelbasedonconvolutionneuralnetworkforinternetofvehicles |