Smartphone User Fingerprinting on Wireless Traffic

TL;DR

This paper introduces U-Print, a passive Wi-Fi traffic analysis system that accurately identifies smartphone users, apps, and actions, revealing significant privacy vulnerabilities in encrypted wireless communications.

Contribution

U-Print is the first system capable of passively recognizing both user identity and app actions from encrypted Wi-Fi traffic using multi-level features and deep learning.

Findings

Achieves 98.4% user identification accuracy

Recognizes 96% of apps and actions in closed-world scenarios

Identifies users with high precision in real-world environments

Abstract

Due to the openness of the wireless medium, smartphone users are susceptible to user privacy attacks, where user privacy information is inferred from encrypted Wi-Fi wireless traffic. Existing attacks are limited to recognizing mobile apps and their actions and cannot infer the smartphone user identity, a fundamental part of user privacy. To overcome this limitation, we propose U-Print, a novel attack system that can passively recognize smartphone apps, actions, and users from over-the-air MAC-layer frames. We observe that smartphone users usually prefer different add-on apps and in-app actions, yielding different changing patterns in Wi-Fi traffic. U-Print first extracts multi-level traffic features and exploits customized temporal convolutional networks to recognize smartphone apps and actions, thus producing users' behavior sequences. Then, it leverages the silhouette coefficient method to determine the number of users and applies the k-means clustering to profile and identify smartphone users. We implement U-Print using a laptop with a Kali dual-band wireless network card and evaluate it in three real-world environments. U-Print achieves an overall accuracy of 98.4% and an F1 score of 0.983 for user inference. Moreover, it can correctly recognize up to 96% of apps and actions in the closed world and more than 86% in the open world.