Abstract

The ever-growing number and diversity of connected devices have contributed to rising network security challenges. Vulnerable and unauthorized devices may pose a significant
security risk with severe consequences. Device-type identification is instrumental in reducing risk and thwarting cyberattacks that may be caused by vulnerable devices. At present, IoT device identification methods use traditional machine learning or deep learning techniques, which require a large amount of labeled data to generate the device fingerprints.
Moreover, these techniques require building a new model whenever a new device is introduced. To address these limitations, we propose a few-shot learning-based approach on
siamese neural networks to identify IoT device-type connected to a network by analyzing their network communications, which can be effective under conditions of insufficient labeled data and/or resources. We evaluate our method on data obtained from real-world IoT devices. The experimental results show the effectiveness of the proposed method even with a small amount of data samples. Besides, it indicates that our approach outperforms IoT Sentinel, the state-of-the-art approach for IoT fingerprinting, by a margin of 10% additional accuracy.