Walls Have No Ears: A Non-Intrusive WiFi-Based User Identification System for Mobile Devices

Abstract

With the development and popularization of WiFi, surfing on the Internet with mobile devices has become an indispensable part of people’s daily life. However, as an infrastructure, WiFi access points (APs) are easily connected by some undesired users nearby. In this paper, we propose NiFi, a non-intrusive WiFi user-identification system based on WiFi signals that enable AP to automatically identify legitimate users in indoor environments, such as home, office, and hotel. The core idea is that legitimate and undesired users may have different physical constraints, e.g., moving area, walking path, and so on, leading to different signal sequences. NiFi analyzes and exploits the characteristics of signal sequences generated by mobile devices. NiFi proposes a practical and effective method to extract useful features and measures similarity for signal sequences while not relying on precise user location information. We implement NiFi on Commercial Off-The-Shelf APs, and the implementation does not require any modification to user devices. The experiment results demonstrate that NiFi is able to achieve an average identification accuracy at 90.83% with true positive rate at 98.89%.