Traffic Classification

Network traffic classification aims to identify the applications or services using network traffic, crucial for network management, security, and quality of service. Current research emphasizes improving classification accuracy and efficiency, particularly for encrypted traffic, using deep learning models like autoencoders, transformers, and graph neural networks, often incorporating techniques like data augmentation, pre-training, and multi-task learning to address challenges such as class imbalance and limited labeled data. These advancements have significant implications for network security, resource allocation, and the development of more intelligent network management systems. Furthermore, research is actively exploring methods to enhance model interpretability and efficiency for deployment in resource-constrained environments.