Secure Region Detection Using Wi-Fi CSI and One-Class Classification

Abstract

Location-based authentication for Internet of Things (IoT) devices can be used to permit activation and participation only to those devices that are in their predefined areas. It secures the devices from being used elsewhere (e.g. misplacement or theft), and also secures the system by preventing non-authorized devices from joining the network. However, absolute location is not required for such purposes; only whether the device is within the designated ‘secure’ region or not matters, which we define as the secure region detection problem. In this work, we propose SWORD, the first secure region detection scheme based on channel state information (CSI) of Wi-Fi and deep one-class classification (OCC) technique. OCC can be trained using data only from the inside of a secure region and no negative reference point is required, a critical advantage considering that outside of a secure region is practically unbounded. Our real-world experiment results show that SWORD can achieve 99.14% true-negative rate (TN, successfully rejecting devices not in secure region) and an acceptable true-positive (TP) of 76.90% for practical usage. Furthermore, there is an user-adjustable trade-off between TN and TP based on application requirement, and TP can be improved to 97.92% without a big loss of TN using simple automatic repeat mechanism.

Keywords

• Secure region detection (SRD)
• one-class classification (OCC)
• Wi-Fi
• channel state information (CSI)
• the IoT authentication