基于机器学习和深度学习的网络流量分类研究

随着互联网技术的不断发展以及网络规模的不断扩大,应用的类别纷繁复杂,新型应用层出不穷。为了保障用户服务质量(QoS)并确保网络安全,准确快速的流量分类是运营商及网络管理者亟须解决的问题。首先给出网络流量分类的问题定义和性能指标;然后分别介绍基于机器学习和基于深度学习的流量分类方法,分析了这些方法的优缺点,并对现存问题进行阐述;接着围绕流量分类线上部署时会遇到的3个问题:数据集问题、新应用识别问题、部署开销问题对相关工作进行阐述与分析,并进一步探讨目前网络流量分类研究面临的挑战;最后对网络流量分类下一步的研究方向进行展望。

Research on network traffic classification based on machine learning and deep learning

With the continuous development of Internet technology and the continuous expansion of network scale,there are many different types of applications,and various new applications have endlessly emerged.In order to ensure the quality of service(QoS)and ensure network security,accurate and fast traffic classification is an urgent problem for both operators and network managers.Firstly,the problem definition and performance metrics of network traffic classification were given.Then,the traffic classification methods based on machine learning and deep learning were introduced respectively,the advantages and disadvantages of these methods were analyzed,and the existing problems were expounded.Next,the related work by focusing on the three problems encountered elaborated and analyzed in traffic classification when considering online deployment:dataset,zero-day application identification and the cost of online deployment,and further discusses the challenges faced by the current network traffic classification researches.Finally,the next research direction of network traffic classification was prospected.