Channel state information based physical layer authentication for Wi‐Fi sensing systems using deep learning in Internet of things networks

Monika Roopak
,
Yachao Ran
,
Xiaotian Chen
,
Gui Yun Tian
,
Simon Parkinson

Sichuan University
,
Newcastle University
,
University of Huddersfield

Research Output:

Contribution to journal

Article

Peer-review

Open access

Abstract

Security problems loom big in the fast-growing world of Internet of Things (IoT) networks, which is characterised by unprecedented interconnectedness and data-driven innovation, due to the inherent susceptibility of wireless infrastructure. One of the most pressing concerns is user authentication, which was originally intended to prevent unwanted access to critical information but has since expanded to provide tailored service customisation. We suggest a Wi-Fi sensing-based physical layer authentication method for IoT networks to solve this problem. Our proposed method makes use of raw channel state information (CSI) data from Wi-Fi signals to create a hybrid deep-learning model that combines convolutional neural networks and long short-term memory networks. Rigorous testing yields an astonishing 99.97% accuracy rate, demonstrating the effectiveness of our CSI-based verification. This technology not only strengthens wireless network security but also prioritises efficiency and portability. The findings highlight the practicality of our proposed CSI-based physical layer authentication, which provides lightweight and precise protection for wireless networks in the IoT.