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%。
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.
作者
张锐
ZHANG Rui(Department of Information Engineering,Zhumadian Vocatiomal and Technical College,Zhumadian 463003,China)
出处
《电信科学》
北大核心
2024年第12期51-62,共12页
Telecommunications Science
基金
河南省科技攻关项目(No.212102210515)。
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
Internet of vehicles
intrusion detection
convolution neural network
particle swarm optimization algorithm
ensemble learning
