摘要
随着网络的不断发展,安全需求的不断提升,加密技术成为保障流量安全的首选,但同时也带来了加密恶意流量的激增,面对复杂多变的网络环境,如何在不解密条件下快速识别其中的恶意流量对提升网络安全防护能力具有重要的意义。以恶意流量分类为研究基础,梳理目前比较流行的加密恶意流量分析识别技术,聚焦基于单维特征和多维特征的流量识别方法,探讨前沿技术在加密恶意流量分析领域的应用研究,为后续研究指出了方向。
With the continuous development of the network and the continuous improvement of security requirements, encryption technology becomes the first choice for ensuring traffic security, but at the same time, it also brings about a surge in encrypted malicious traffic. In the face of complex and changeable network environment, how to quickly identify malicious traffic without decryption is of great significance to improve network security protection capabilities. Based on malicious traffic classification, this paper sorts out currently popular encrypted malicious traffic analysis and identification technologies, focuses on traffic identification methods based on single-dimensional features and multi-dimensional features, and discusses the application of cutting-edge technologies in the field of encrypted malicious traffic analysis,and points out the direction for subsequent research.
作者
陆勰
徐雷
张曼君
张超
LU Xie;XU Lei;ZHANG Manjun;ZHANG Chao(Research Institute of China United Network Communications Group Co.,Ltd.,Beijing 100048,China;Network Department of China United Network Communication Group Co.,Ltd.,Beijing 100035,China)
出处
《信息安全与通信保密》
2022年第3期71-79,共9页
Information Security and Communications Privacy
关键词
加密
恶意
流量
特征
机器学习
encryption
malicious
traffic
feature
machine learning
