基于图像识别算法的网络流量分类研究

Research on Network Traffic Classification Based on Image Recognition Algorithm

网络流量分类是网络规划和网络空间安全的一个关键环节,基于端口号和深度包的流量分类方法面临端口可变和流量加密的挑战,基于统计特征的流量分类方法过分依赖特征选取,在扩展性方面存在限制,且手动进行特征选择直接影响分类结果。在日趋复杂的网络环境中,将原始网络流量图像化,使用深度学习的方法实现特征的自动选取成为新的研究领域。文章对基于图像识别算法的流量分类方法进行分析和比较,最后对基于图像识别算法的流量分类技术面临的挑战与未来发展方向进行探讨。

Network traffic classification is a key link in network planning and network space security,The traffic classification methods based on port number and deep packet faces the challenges of port variability and traffic encryption.The traffic classification methods based on statistical features rely too much on feature selection,which has limitations in scalability,and manual feature selection directly affects the classification results.In the increasingly complex network environment,it has become a new research field to image the original network traffic and use deep learning method to realize automatic feature selection.This paper analyzes and compares the traffic classification methods based on image recognition algorithm,and finally discusses the challenges and future development direction of traffic classification technology based on image recog-nition algorithm.