Distributed and pervasive web services have become a major platform for sharing information. However, the hypertext transfer protocol secure (HTTPS), which is a crucial web encryption technology for protecting the information security of users, creates a supervisory burden for network management (e.g., quality-of-service guarantees and traffic engineering). Identifying various types of encrypted traffic is crucial for cyber security and network management. In this paper, we propose a novel deep learning model called BGRUA to identify the web services running on HTTPS connections accurately. BGRUA utilizes a bidirectional gated recurrent unit (GRU) and attention mechanism to improve the accuracy of HTTPS traffic classification. The bidirectional GRU is used to extract the forward and backward features of the byte sequences in a session. The attention mechanism is adopted to assign weights to features according to their contributions to classification. Additionally, we investigate the effects of different hyperparameters on the performance of BGRUA and present a set of optimal values that can serve as a basis for future relevant studies. Comparisons to existing methods based on three typical datasets demonstrate that BGRUA outperforms state-of-the-art encrypted traffic classification approaches in terms of accuracy, precision, recall, and F1-score.

分布式和普及的Web服务已经成为共享信息的主要平台。但是，超文本传输​​协议安全（HTTPS）是一种用于保护用户信息安全的至关重要的Web加密技术，它给网络管理（例如服务质量保证和流量工程）带来了管理负担。识别各种类型的加密流量对于网络安全和网络管理至关重要。在本文中，我们提出了一种称为BGRUA的新型深度学习模型，用于准确识别在HTTPS连接上运行的Web服务。BGRUA利用双向门控循环单元（GRU）和注意机制来提高HTTPS流量分类的准确性。双向GRU用于提取会话中字节序列的前向和后向特征。采用注意机制，根据特征对分类的贡献为特征分配权重。此外，我们研究了不同超参数对BGRUA性能的影响，并提出了一组最佳值，这些值可作为未来相关研究的基础。与基于三个典型数据集的现有方法的比较表明，BURUA在准确性，准确性，查全率和F1分数方面优于最新的加密流量分类方法。