摘要
通信网络中的异常流量种类繁多,包括各种类型的攻击、恶意行为等,并且具有非常复杂、高层次的抽象特征,无法捕捉其中的局部异常行为,使得识别异常流量的效率较低。为此,引入卷积神经网络,提出基于深度学习的通信网络异常流量快速识别方法。采用Wireshark工具,获取并融合网络流量数据,利用加权平均滤波方法,对其均衡化处理,选取深度学习中的卷积神经网络(Convolutional Neural Network,CNN)作为异常流量特征提取算法,在卷积层中,输入网络流量数据中的局部特征,实现对异常流量特征提取。计算异常流量特征的整体相似度,设定异常流量阈值,通过通信网络中流量的异常概率实现对异常流量快速识别。实验结果表明,所提算法应用下的召回率最高达0.96,在200byte数据量的情况下,识别延时仅为9.2ms,为快速识别和处理异常流量的应用场景提供有效的支持。
characteristics,which cannot capture the local abnormal behavior,making the efficiency of identifying abnormal traffic low.Therefore,the convolutional neural network is introduced to propose a fast identification method of abnormal traffic in the communication network based on deep learning.Using Wireshark tool to obtain and fuse network traffic data,using the weighted average filtering method,select the convolution neural network (Convolutional Neural Network,CNN)as the abnormal flow feature extraction algorithm,input the local features in the convolution layer,realize the abnormal flow feature extraction.The overall similarity of the abnormal traffic characteristics is calculated,the abnormal traffic threshold is set,and the abnormal traffic is recognized quickly through the abnormal probability of the traffic in the communication network.The experimental results show that the recall rate of the proposed algorithm is up to 0.96,and in the case of 200 byte data,the identification delay is only 9.2ms,which provides effective support for the application scenario of quickly identifying and processing abnormal flow.
作者
祝荣华
周玲军
ZHU Ronghua;ZHOU Lingjun(Zhujiang College Of South China Agricultural Univer,Guangzhou Guangdong 510900 China)
出处
《长江信息通信》
2024年第12期96-98,共3页
Changjiang Information & Communications
关键词
卷积神经网络
通信网络
特征提取
异常流量识别
convolutional neural network
communication network
feature extraction
abnormal traffic identification
作者简介
祝荣华(1986-),女,湖北武汉人,研究生,讲师,研究方向:计算机科学与技术;通讯作者:周玲军(1984-),男,湖北黄冈人,研究生,讲师,研究方向:思想政治教育。
