## 1 Introduction  
The continuous development and popularity of the Internet of things (IoT) has enabled different sectors of the economy to combine traditional business models with the Internet, creating new solutions, e.g., smart transportation, environmental protection, e-government, safe home, industrial monitoring, etc., [1, 2]. However, a large number of interconnected devices leads to poor synchronization protection and management challenges, which may affect the overall security and reliability of the network. Therefore, to provide better services for network users and improve the effectiveness of network management, an efficient network traffic classification (NTC) and supervision is necessary. In NTC, the network packets are captured to determine their identity. The effective NTC can guarantee the quality of network service and intrusion detection system (IDS) [3, 4, 5]. In the era of big data, NTC has to handle a large quantity of network traffic data, which leads to a high computational complexity and slow classification time [6]. Early NTC methods used Transmission Control Protocol (TCP) or User Datagram Protocol (UDP) ports to distinguish between different network traffic [7]. These methods are simple to use and have a high classification efficiency. However, with the continuous development of the Internet, there are many applications in the network environment that do not follow the established port label or directly hide the ports used by applications. This led to the gradual elimination of port-based traffic classification methods and the rise of deep packet inspection (DPI)-based classification technology. DPI-based technologies are not restricted by network ports and use the unique identifiers in the application layer directly to classify traffic [8]. However, because the use of application-layer data may violate user privacy, and the method cannot recognize encrypted data, DPI-based classification technology has gradually faded out of the NTC field. To address these challenges, researchers have introduced feature selection techniques [9]. Feature selection is the process of selecting the most effective subset of features that compose the entire dataset, and a good subset of features is one that contains features that are highly related to but not related to each other. These features are also the most common reason why network classification systems use feature selection technology. Collected network traffic data usually include information such as source IP, destination IP, source port, destination port, and number of bytes, which are used to distinguish different types of network traffic. Classifying network traffic based on these features technically compensates for the basic technical deficiencies of traditional port-based and DPI-based detection. At the same time, these features do not contain valid information on the message, thus avoiding the disclosure of user privacy. However, there are many features of network traffic, and the number of available features identified in current academic research has reached 240 [10]. For some network traffic, it is too redundant to use all features for classification, which will not only increase the complexity of the classification model, but also reduce the classification accuracy. In this paper, we propose an adversarial autoencoder (CAAE) network to extract the most relevant features of complex network traffic. The combination of adversarial training and autoencoder can greatly reduce the feature dimension of the original network traffic, and at the same time, filter out the most effective feature information. The basic idea of adversarial training is to continuously generate and learn adversarial samples during network training. For example, according to the minimax formula, adversarial samples are searched for by maximizing the loss function in the inner layer, and then adversarial samples are learned in the outer layer to minimize the loss function.