GBDT与LR融合模型在加密流量识别中的应用

随着网络应用服务类型的多样化以及网络流量加密技术的不断发展，加密流量识别已经成为网络安全领域的一个重大挑战。传统的流量识别技术如深度包检测无法有效地识别加密流量，而基于机器学习理论的加密流量识别技术则表现出很好的效果。因此,本文提出一种融合梯度提升决策树算法(GBDT)与逻辑回归(LR)算法的加密流量分类模型，使用贝叶斯优化(BO)算法进行超参数调整，利用与时间相关的流特征对普通加密流量与VPN加密流量进行识别，实现了整体高于90%的流量识别准确度，与其他常用分类模型相比拥有更好的识别效果。

Application of Fusion Model of GBDT and LR in Encrypted Traffic Identification

With the diversification of network application service types and the continuous development of traffic encryption technology, encrypted traffic identification has become a major challenge in the field of network security. Traditional traffic identification techniques, such as deep packet inspection, cannot effectively identify encrypted traffic, while the identification technology based on machine learning theory has shown good results. For this, an optimized encrypted traffic classification model based on the fusion of GBDT and LR is proposed, in which Bayesian optimization (BO) algorithm is used for hyperparameter tuning. By using the time-related flow features to identify common encrypted traffic and VPN encrypted traffic, it obtains an overall accuracy more than 90%, which gets better recognition effect than other common classification models.