摘要
随着互联网技术的广泛普及,网络安全问题也随之增加。作为网络系统的主要防御手段之一,对网络流量进行异常检测从过去基于流量负载特征和基于异常特征库匹配的检测方式,逐渐向基于机器学习、深度学习的分类方法转变。文章首先提出一种基于数据包数目的网络流量数据样本划分方法,然后组合使用深度学习中的卷积神经网络和循环神经网络提出一种基于卷积循环神经网络的网络流量异常检测算法,该算法能更充分地提取网络流量数据在空间域和时间域上的特征;最后使用公开网络流量数据集进行流量异常检测实验。实验得到了很高的精度、召回率和准确率,验证了文章方法的有效性。
With the wide spread of Internet technology,network security issues also increase.As one of the main defense means of the network system,the method of anomaly detection of network traffic has gradually changed from the detection methods based on traffic load characteristics and anomaly feature database matching to classification methods based on machine learning and deep learning.Firstly,this paper proposes a network traffic data sample partition method based on the number of data packets,and then combining convolutional neural network and recurrent neural network in deep learning,proposes a network traffic anomaly detection algorithm based on convolutional recurrent neural network,which can more fully extract the characteristics of network traffic data in spatial domain and time domain.Finally,this paper uses the public network traffic data set to detect traffic anomaly.High precision,recall and accuracy are obtained by experiments,which verifies the effectiveness of the proposed method.
作者
徐洪平
马泽文
易航
张龙飞
XU Hongping;MA Zewen;YI Hang;ZHANG Longfei(China Academy of Launch Vehicle Technology,Beijing 100076,China)
出处
《信息网络安全》
CSCD
北大核心
2021年第7期54-62,共9页
Netinfo Security
基金
国家自然科学基金[62072025]。
关键词
流量异常检测
卷积循环神经网络
样本生成
traffic anomaly detection
convolutional recurrent neural network
sample generation
