MUFFIN: A Meta-Knowledge Decoupling-Based Approach to Few-Shot IoT Traffic Classification

Traditional machine learning-based approaches to Internet of Things (IoT) traffic classification are hindered by the need for a large number of labeled flows in real-world network environments. To cope with this problem, some few-shot traffic classification approaches have been proposed to achieve accurate classification by comparing the similarity of flows to the given labeled flows. These approaches train neural networks to concurrently learn feature extraction and feature comparison meta-knowledge from sufficient labeled flows. However, they conflate these two types of meta-knowledge and ignore their differences. We propose MUFFIN, a meta-knowledge decoupling-based approach to few-shot IoT traffic classification. MUFFIN is based on our key insight that feature extraction and feature comparison meta-knowledge are two different types of meta-knowledge with distinct learning goals. Therefore, MUFFIN decouples their learning in the meta-knowledge phase and introduces a masked autoencoder as its feature extractor to enhance the learning of feature extraction meta-knowledge. Extensive experiments conducted on three publicly available datasets demonstrate the classification ability and robustness of MUFFIN. Furthermore, a comparison with five state-of-the-art few-shot traffic classification approaches reveals that MUFFIN outperforms them in classification accuracy and requires only 20 labeled flows.