LAUSR

IoT traffic classification is an important step in network management. Efficient and accurate IoT traffic classification helps Internet Service Providers provide high-quality services to network users. At present, popular IoT traffic classification methods are using traditional machine learning or deep learning algorithm, which relies on a large amount of labeled traffic to construct the traffic-level fingerprinting. However, it is worth noting that some classes of IoT devices only generate limited labeled traffic when they are working, and this limited labeled traffic is insufficient for the aforementioned classification methods. In this letter, we propose Festic, a few-shot learning based approach to IoT traffic classification. Festic can accurately classify IoT traffic under conditions of insufficient labeled traffic. We evaluate Festic on two publicly available datasets, and the experimental results show that Festic has excellent classification accuracy and outperforms the state-of-the-art traffic classification methods.