# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 1 Introduction  
The advancement of science and technology and ultra-high-speed networks is accompanied by the rise of various applications. With the advancement of modern network technologies such as cloud computing and edge computing, research on efficient network management has been actively conducted. Among them, network traffic classification research is one of the key factors for efficient network management [1; 2; 3; 4; 5]. Traffic classification methods encompass traditional, signature-based, learning-based, and transformer-based approaches [3; 4; 5; 6; 7; 8]. Traditional methods rely on port-based and payload-based techniques. Port-based classification uses origin and destination ports, offering simplicity and low computational cost, but it faces limitations with dynamic ports. Payload-based classification utilizes fixed payload content, providing simplicity and high performance, but it is susceptible to encrypted traffic and struggles to adapt to new protocols [9]. Signature-based methods classify traffic based on specific patterns or signatures, demonstrating high performance for defined signatures. However, they face challenges in adapting to changing patterns and encrypted traffic. Overall, network traffic classification research plays a key role in enhancing efficient network management amid evolving technological landscapes. With recent advances in AI and technologies, most studies are using learning-based methods [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. Learning-based methods utilize machine learning (ML) and deep learning (DL) algorithms to learn and classify traffic. Models are trained on large amounts of traffic data to identify specific patterns or trends, which are then used to predict or classify new traffic. Due to these advantages, many studies have utilized learning-based methods, and they have improved performance in many areas. Transformer-based methods are one of the more recent deep learning techniques to emerge, applying structures that have performed particularly well in natural language processing (NLP) for traffic classification [33; 34; 35; 36]. The self-attention mechanism of the transformer effectively learns the global dependencies of sequence data, which has shown promising performance in a variety of applications. For instance, the field of NLP has witnessed a notable advancement with the introduction of bidirectional encoder representation from transformers (BERT) pre-training models [35; 36]. BERT has demonstrated high performance in many fields and can be effectively applied to downstream tasks by learning relationships and structures for unbiased data from unlabeled data. In line with this trend, many studies have been conducted in the field of network traffic classification by applying transform-based methods. These methods have shown higher performance than traditional learning-based methods. With the growing concerns regarding personal privacy and security, most applications now utilize encrypted traffic [37; 38; 39]. As encrypted communications protect payload content, traditional traffic classification methods have become inapplicable. Researchers use publicly available encrypted traffic datasets such as ISCX 2016 VPN/Non-VPN [40]. for encrypted traffic classification studies. In these encrypted traffic classification studies, public datasets are mainly divided into intrusion detection systems (IDS) and application classification, each of which is in turn divided into specific tasks. For example, the ISCX 2016 VPN/Non-VPN, which is often used for application classification studies, consists of three tasks: encapsulation, category, and application. Traffic classification methods are categorized into single-task learning (STL) and multi-task learning (MTL) based on the target data task. STL focuses on training a model for a specific task in machine learning, enhancing performance by learning task-specific features and patterns. However, this optimized model may have limited applicability to other tasks.