Exploring Wi-Fi Privacy Disclosure: A Novel Approach to User Identity Prediction Based on Traffic Multi-level Information
Yiwen Li, Shanshan Wang, Zhenxiang Chen, Xueyang Cao, Yadi Han · 2023
Nowadays, people are accustomed to network communication via Wireless Fidelity (Wi-Fi) when using mobile phones and IoT devices in fixed places (such as homes and dormitories). This means that a large amount of Wi-Fi traffic data will be generated every day. This paper focuses on the leakage of user identity privacy in traffic, namely: can user identity attributes be inferred from real home Wi-Fi traffic data? If malicious attackers can obtain user identities so "conveniently", it means that the success rate of malicious activities such as phishing and fraud can be easily increased. Therefore, to explore this question, we propose a new method for predicting user attributes based on user network behavior similarity. Then we collected real world data for verification and compared it with other methods. Finally, this paper also analyzes the current response methods for Wi-Fi privacy disclosure and points out the problems and future research directions.