深度学习下的网络异常流量检测模型设计  

Network Abnormal Traffic Detection Model Design in Deep Learning

作　　者：徐静 XU Jing(Pingxiang Health Vocational College,Pingxiang Jiangxi 337000,China)

机构地区：[1]萍乡卫生职业学院,江西萍乡337000

出　　处：《信息与电脑》2024年第15期8-11,共4页Information & Computer

摘　　要：文章通过对网络安全威胁检测问题进行研究,提出了一种基于卷积神经网络的异常网络流量检测模型,同时对可能威胁网络安全的异常网络流量进行了检测,并通过分类确定了异常流量的类型。测试结果显示,该模型对不同类别的异常网络流量进行检测和分类的平均准确率达到了99.24%,平均F1值达到了99.23%,具有可行性和有效性,且综合性能良好。The article proposes an abnormal network traffic detection model based on convolutional neural network by studying the problem of network security threat detection,while detecting abnormal network traffic that may threaten network security and determining the types of abnormal traffic through classification.The test results show that the model detects and categorizes different categories of anomalous network traffic with an average accuracy of 99.24%and an average F1 value of 99.23%,which is feasible and effective,and has good overall performance.

关 键 词：深度学习 卷积神经网络 网络安全 异常网络流量 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]