Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT

TLDR

User authentication is essential in corporate and home settings, and while smart‑city technologies broaden its scope, traditional methods rely on specialized hardware or inconvenient wearables. The study proposes a device‑free authentication method that uses ubiquitous WiFi signals from IoT devices. It extracts features from WiFi channel state information during walking and stationary activities and trains a deep‑learning model, validated in a university office and an apartment. The system attains 94 % accuracy for walking and 91 % for stationary activities across 11 subjects.

Abstract

User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This paper supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV and thermostat, etc. The proposed system utilizes the WiFi signals to capture unique human physiological and behavioral characteristics inherited from their daily activities, including both walking and stationary ones. Particularly, we extract representative features from channel state information (CSI) measurements of WiFi signals, and develop a deep learning based user authentication scheme to accurately identify each individual user. Extensive experiments in two typical indoor environments, a university office and an apartment, are conducted to demonstrate the effectiveness of the proposed authentication system. In particular, our system can achieve over 94% and 91% authentication accuracy with 11 subjects through walking and stationary activities, respectively.