FITIC: A Few-shot Learning Based IoT Traffic Classification Method

• Main Authors: Jia, Wenxu, Wang, Yipeng, Lai, Yingxu, He, Huijie, Yin, Ruiping
• Format: Conference Proceeding
• Language: English
• Subjects: Computational modeling; Few-shot Learning; Internet of Things; Machine learning; Network Traffic Classification; Object recognition; Payloads; Web and internet services
• ISSN: 2637-9430
• DOI: 10.1109/ICCCN54977.2022.9868887

With the rapid development and wide application of Internet of Things (IoT) technology, Internet Service Providers need to accurately classify IoT traffic to provide hierarchical network management and network protection for highly het-erogeneous IoT devices. Currently, popular traditional machine learning and deep learning-based approaches to IoT traffic classification require large amounts of labeled traffic to build classification models. However, in practice simple IoT traffic with simple operating modes can be identified with only a small amount of labeled traffic and some classes of IoT devices only generate a limited amount of traffic, therefore, the aforementioned methods is not applicable in such scenarios. In this paper, we propose FITIC, a novel IoT traffic classification method based on few-shot learning. FITIC proposes a feature construction method for IoT traffic characteristics and can classify IoT traffic with only a limited number of labeled traffic samples. We evaluate FITIC on two publicly available datasets, and the experimental results show that FITIC has excellent classification accuracy and outperforms the state-of-the-art traffic classification methods.