面向车联网的基于卷积神经网络的入侵检测模型
An intrusion detection model based on convolution neural network for Internet of vehicles
张锐1
作者信息
- 1. 驻马店职业技术学院信息工程学院,河南 驻马店 463003
- 折叠
摘要
为了提高车联网入侵检测的准确率,提出了基于超参数优化卷积神经网络的集成的入侵检测系统(hyper-parameter optimization convolution neural network-based ensemble Intrusion detection system,CNES)模型.CNES模型利用卷积神经网络构建集成学习的基学习器,并利用粒子群优化算法优化卷积神经网络的超参数,进而优化卷积神经网络模型.利用平均法和级联法的集成策略构建集成学习模型,提高检测攻击的准确率.通过车内网络数据集Car-Hacking和车外网络数据集CICIDS2017验证CNES模型的性能.性能分析表明,提出的CNES模型有效地提高了检测网络攻击的性能.在Car-Hacking数据集上,CNES模型的F1值达到100%.
Abstract
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.
关键词
车联网/入侵检测/卷积神经网络/粒子群优化算法/集成学习Key words
Internet of vehicles/intrusion detection/convolution neural network/particle swarm optimization algo-rithm/ensemble learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据