期刊导航

论文摘要

基于卷积神经网络的网络流量识别技术研究

The Research of network traffic identification based on Convolutional neural network

作者:李勤(四川大学计算机学院);师维(四川大学计算机学院);孙界平(四川大学计算机学院);董超(四川大学计算机学院);曲天舒(英国利物浦大学)

Author:LI Qin(College of Computer Science, Sichuan University);SHI Wei(College of Computer Science, Sichuan University);SUN Jie-Ping(College of Computer Science, Sichuan University);DONG Chao(College of Computer Science, Sichuan University);QU Tian-Shu(University of Livepool, U. K.)

收稿日期:2016-06-30          年卷(期)页码:2017,54(5):959-964

期刊名称:四川大学学报: 自然科学版

Journal Name:Journal of Sichuan University (Natural Science Edition)

关键字:网络流量;流量识别;卷积神经网络;深度学习

Key words:Internet traffic; traffic identification; Convolutional neural network; deep learning

基金项目:国家自然科学基金

中文摘要

近年来,深度包检测技术和基于统计特征的网络流量识别技术迅速发展,但它们分别存在不能识别加密流量和依赖人对特征主观选择的缺陷。文章提出了基于卷积神经网络的流量识别方法,将网络数据按照一定的规则转换为灰度图像进行识别,并根据TCP数据包的有序性和UDP数据包的无序性,对原始的网络数据进行了扩展,以进一步提高识别率。实验数据表明,该方法对应用程序和应用层协议两个层次的网络流量具有较高的检测率。

英文摘要

In recent years, the deep packet inspection technology and traffic identification technology based on the statistical characteristics of data packet have developed rapidly. But they have some disadvantages. The deep packet inspection technology can’t identify the encrypted network traffic, and the other technology heavily relies on subjectively chosen statistical features. A network traffic identification method based on convolutional neural network algorithm is proposed in this paper. According to certain rules, the network data is converted to gray images. In order to improve the recognition rate, the original network data is extended according to the order of the TCP packets and the disorder of the UDP packets. Experimental data shows that this method has a high detection rate both in the application and application layer protocol.

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