Toward Open-Set Specific Emitter Identification Using Auxiliary Classifier Generative Adversarial Network and OpenMax

Abstract

Specific emitter identification (SEI) based on unavoidable hardware impairments of transmitters has emerged as a potential technology for physical layer authentication of Internet of Things (IoT) devices. The integration with deep learning has significantly accelerated the advancement of SEI in recent years, showcasing its strong application prospects. However, most SEI works are designed for close-set scenarios, leading to misclassification of unknown transmitters outside the close-set, which poses a serious security risk to the authentication system. To address this issue, a SEI framework with anomaly detection capabilities is essential. In this paper, we propose a novel open-set SEI framework that leverages the Auxiliary Classifier Generative Adversarial Network (ACGAN) to generate outlier samples, introduces OpenMax to obtain calibrated activation vectors (AV), and applies a threshold on the category confidence. Through the proposed triple anomaly detection condition, our SEI framework is endowed with enhanced capabilities for identifying anomalous transmitters. We evaluate the framework using 10 known and 6 unknown WiFi transmitters and achieve a high F1-Score of 0.911 and a low False Positive Rate (FPR) of 0.063 in an anomaly detection task.