UID-Auto-Gen: Extracting Device Fingerprinting from Network Traffic
Haoyu Bin, Sen Zhao, Zhi Li, Nan Yu, Rongrong Xi, Hongsong Zhu, Limin Sun · 2023
The number of Internet device vulnerabilities has been quickly rising in recent years, rendering an explosion of network attacks. Device fingerprinting serves as the primary means for vulnerability awareness and attacker tracking. The current device fingerprinting approach can only achieve model-level identification within the Internet scope or individual-level identification for specific protocols (e.g., SSL) or scenarios (e.g., LAN). However, it is still difficult for these methods to achieve individual-level identification on a global Internet scale. In this paper, we propose a fingerprint extraction approach that is accurate to the individual level of the device by using a combination of clustering, multiple sequence alignment, and based on the geographic location stability of the device. In a continuous 3-month observation for several cities around the world, at least 1.54% of devices can be accurately extracted with unique IDs, with an accuracy rate of 99.30%, which is capable of being used in production environments.