# Deep Learning-Based Efficient Analysis for Encrypted Traffic
## 1 Introduction
In the actual communication process, the payload in the data packet will be encrypted using a symmetric cipher and deep packet inspection technology cannot be used to identify the traffic type corresponding to the data packet, but it can be further analyzed. The identification task of encrypted traffic can be conducted to some extent by using machine learning methods to adapt a series of statistical features, or spatial and temporal features of a single data packet, during the transmission of a data stream. With the development of deep learning technology, convolutional neural networks (CNNs) [11] and recurrent neural networks (RNNs) [12] have provided numerous mature solutions for computer vision and natural language processing. Using deep learning techniques, features in traffic can be automatically extracted without feature engineering through multiple nonlinear fully connected layers of artificial neural networks with excellent generalization capabilities. This study proposes a deep learning-based solution for encrypted traffic identification and clarifies the subject background and purpose of the proposed deep learning-based encrypted traffic identification system. The aim of this research is to design and implement a deep learning-based system for accurately identifying encrypted traffic. Initially, we construct a CNN model to learn spatial characteristics from the training and then evaluate its performance in identifying encrypted traffic using the testing dataset. Next, we introduce an enhanced LSTM model that utilizes the feature generated by the convolutional layer of our one-dimensional CNN model as input. This allows us to utilize high-level spatial features and extract temporal characteristics among them. These modifications enable us to develop a comprehensive model for accurately identifying encrypted traffic. This system effectively accomplishes end-to-end encrypted traffic identification by automatically extracting temporal features from packets. The contributions of this work can be summarized as follows. (1) This paper aims to develop a deep learning-based encryption traffic identification system that automatically extracts spatial and temporal features from data packets to achieve end-to-end encryption traffic identification. (2) The overall framework of the encrypted traffic identification system based on deep learning is proposed, and the relevant details of the data flow are analyzed. We also optimize the efficiency of storage space and running time for data preprocessing. (3) The implementation of a comprehensive model for identifying encrypted traffic was conducted, and a quantitative comparison with traditional detection was carried out This study validates the proposed C-LSTM model for extracting spatial and temporal features from data packets. This paper is organized as follows: We illustrate the introduction, contributions, and related works in Section 1. Section 2 illustrates the preliminaries of the work. The system framework is described in Section 3. Section 4 describes the proposed scheme and performance analysis, respectively. Finally, the paper is concluded in Section 5.