A Novel Framework for Open-Set Authentication of Internet of Things Using Limited Devices

Abstract

The Internet of Things (IoT) is promising to transform a wide range of fields. However, the open nature of IoT makes it exposed to cybersecurity threats, among which identity spoofing is a typical example. Physical layer authentication, which identifies IoT devices based on the physical layer characteristics of signals, serves as an effective way to counteract identity spoofing. In this paper, we propose a deep learning-based framework for the open-set authentication of IoT devices. Specifically, additive angular margin softmax (AAMSoftmax) was utilized to enhance the discriminability of learned features and a modified OpenMAX classifier was employed to adaptively identify authorized devices and distinguish unauthorized ones. The experimental results for both simulated data and real ADS–B (Automatic Dependent Surveillance–Broadcast) data indicate that our framework achieved superior performance compared to current approaches, especially when the number of devices used for training is limited.

Internet of Things: Development Intelligent Programmable IoT Controller for Emerging Industry Applications

“…The IoT enables a wide range of devices to communicate and alternate information with each other, enabling a wide range of industry sectors to be enhanced [ 29 ]. Smart factory equipment requires the installation of intelligent sensors to collect a large amount of data related to the production process, making data critical in increasing the competitiveness of the industry [ 30 ].…”