不同分类器模型对网络传输数据异常识别方法  

Different Classifier Models for Anomaly Recognition of Network Transmission Data

作　　者：罗艳芳 LUO Yanfang(Wuchang Institute of Technology,Wuhan Hubei 430065,China)

机构地区：[1]武昌工学院,湖北武汉430065

出　　处：《信息与电脑》2023年第9期211-213,共3页Information & Computer

摘　　要：随着互联网技术的发展,网络传输已经成为人们生活中必不可少的一部分。但是,网络传输数据的复杂性和不确定性,导致网络传输数据的异常问题也越来越突出,因此如何识别和处理网络传输数据的异常已经成为研究的重点。近年来,深度学习技术的发展为网络传输数据的异常检测提供了新的方法。深度学习技术可以自动地从大量的数据中提取出有效的特征,并使用这些特征检测异常,因此文章探讨影响基于深度学习的网络传输数据异常识别的因素。With the development of Internet technology,network transmission has become an essential part of people's life.However,the complexity and uncertainty of network transmission data lead to the abnormal problem of network transmission data is becoming more and more prominent,so how to identify and deal with the anomaly of network transmission data has become the focus of research.Recently,the development of deep learning technology has provided new methods for anomaly detection of network transmitted data.Deep learning technology can automatically extract effective features from large amounts of data and use these features to detect anomalies,so the paper explores the factors affecting the exception identification of network transmission data based on deep learning.

关 键 词：深度学习 网络传输 数据异常 识别 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]