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

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. Internet of Things (IoT) security concerns, particularly regarding user authentication, are addressed by our proposed Wi-Fi sensing-based physical layer authentication. The solution, integrating convolutional neural networks and long short-term memory networks with raw channel state information data, achieves an impressive 99.97% accuracy, offering effective and portable protection for wireless networks in the IoT landscape. image