摘要
随着高校信息化建设从数字校园到智慧校园的过渡,高校逐步实现数据服务和应用的全覆盖,同时网络安全问题日渐突出。文章分析了校园网普遍存在的安全威胁,根据网络威胁在网络流量中的异常表现,对卷积神经网络架构、训练过程进行了研究,建立了基于卷积神经网络的网络流量异常检测模型,并对模型建立后的数据准备、分类识别方法进行了探讨,实现了网络流量的分类。
With the transition of informatization construction from digital campus to wisdom campus,colleges gradually realize the full coverage of data services and applications.At the same time,the problem of network security is becoming more and more prominent.This paper analyzes the common security threats in campus network,studies the architecture and training process of convolutional neural network according to the abnormal performance of network threats in network traffic,establishes the network traffic anomaly detection model based on convolutional neural network,discusses the data preparation,classification identification methods after the model is established,and realizes the classification of network traffic.
作者
英锋
YING Feng(Liupanshui Normal University,Liupanshui 553004,China)
出处
《现代信息科技》
2021年第12期94-96,100,共4页
Modern Information Technology
关键词
卷积神经网络
网络流量
异常检测
convolutional neural network
network traffic
anomaly detection
