To protect user privacy (e.g., IP address and sensitive data in a packet), many traffic protection methods, like traffic obfuscation and encryption technologies, are introduced. However, these methods have been used by attackers to transmit malicious traffic, posing a serious threat to network security. To enhance network traffic supervision, this paper proposes a new traffic classification model based on Gaussian mixture models and hidden Markov models, named MGHMM. To evaluate the effectiveness of the proposed model, we first classify protocols and identify the obfuscated traffic by experiments. Then, we compare the classification performance of MGHMM with that of the latest Vector Quantiser-based traffic classification algorithm. On the basis of the experiment, the relation between the classification and the number of hidden Markov states, and the number of mixture of Gaussian distributions required to describe the hidden states, are analyzed.

为了保护用户隐私（例如，数据包中的IP地址和敏感数据），引入了许多流量保护方法，例如流量混淆和加密技术。但是，攻击者已使用这些方法来传输恶意流量，这对网络安全构成了严重威胁。为了加强网络流量监管，本文提出了一种基于高斯混合模型和隐马尔可夫模型的流量分类模型，称为MGHMM。为了评估所提出模型的有效性，我们首先对协议进行分类，并通过实验确定混淆的流量。然后，我们将MGHMM的分类性能与最新的基于矢量量化器的流量分类算法进行比较。在实验的基础上，分类与隐马尔可夫状态数之间的关系，