A mutual detection method for wireless IoT devices based on encrypted traffic analysis
Yilin Li, Liang Wang, Shaokang Zhang, Meng Wang, Haibo Liu · The Computer Journal · 2025
Abstract With the rapid proliferation of wireless Internet of Things (IoT) devices, there is a growing concern about the potential misuse of wireless IoT devices for unauthorized sensing and monitoring of daily activities. Individuals are increasingly aware of the risks related to unidentified wireless IoT devices. Therefore, there is an urgent need for a comprehensive approach to improve the transparency of wireless IoT devices. In view of this, we propose a mutual detection and identification approach for autonomously detecting and identifying wireless IoT devices based on encrypted traffic analysis. We develop an adaptive device detection algorithm that uses a mutual detection mechanism among devices to facilitate distributed detection tasks and gather encrypted traffic data. We also design a two-stage device identification method to identify the devices. Through experiments conducted on two publicly available datasets, the study achieved a classification accuracy of $ \mathbf{100\%}$ in distinguishing wireless IoT devices from other device types, as well as a high accuracy of $\mathbf{99.59\%}$ in identifying specific types of wireless IoT devices. These results highlight the efficacy and potential of the proposed method in enhancing the security and transparency of wireless IoT ecosystems.