计算机工程与应用2019，Vol.55Issue(3)：90-95,153,7.DOI:10.3778/j.issn.1002-8331.1712-0021
用于网络入侵检测的多尺度卷积CNN模型
Multiscale Convolutional CNN Model for Network Intrusion Detection
摘要
Abstract
In view of the great achievements of convolutional neural networks in many fields such as computer vision, a method of applying multi-scale convolutional neural networks to the field of network intrusion detection is proposed. This method converts the network data in IDS into data that the convolutional neural network can input, uses different scales of convolution to verify a large number of high-dimensional unlabeled original data for different levels of feature extraction, and then uses the BN method to optimize the learning rate of the network structure. The optimal feature representation of raw data. Experiments using the KDDcup99 data set for experimental testing, compared with the classic model, the results show that the MSCNN model not only has a fast convergence rate, but also the false detection rate is reduced by 4.02% on average, and the accuracy rate is increased by 4.38% on average. Therefore, the MSCNN method is a feasible and efficient method and provides a brand-new idea for the field of network intrusion detection systems.关键词
入侵检测/深度学习/卷积神经网络/BN算法/多尺度卷积Key words
intrusion detection/deep learning/convolutional neural networks/BN algorithm/multiscale convolution分类
信息技术与安全科学引用本文复制引用
刘月峰,王成,张亚斌,苑江浩..用于网络入侵检测的多尺度卷积CNN模型[J].计算机工程与应用,2019,55(3):90-95,153,7.基金项目
贵州省科学技术基金(No.[2015]2076,No.[2016]7018). （No.[2015]2076,No.[2016]7018）
