Specific Emitter Identification Based on a Hybrid Deep Neural Network for ACARS Authentication

Abstract

With the increasing variety and quantity of aircraft, there is a potential threat to the security of the Aircraft Communications Addressing and Reporting System (ACARS) due to the lack of reliable authentication measures. This paper proposes a novel specific emitter identification (SEI) algorithm based on a hybrid deep neural network (DNN) for ACARS authentication. Our deep learning architecture is a combination of Deep Residual Shrinkage Network (DRSN), Bidirectional-LSTM (Bi-LSTM), and attention mechanism (AM), which perform the functions of local and global feature learning and feature focusing, respectively, so that the individual information hidden in the signal waveform can be thoroughly mined. We introduce soft thresholding as a nonlinear transformation in the DSRN to enhance robustness against noise and adopt a low-cost training strategy for new data using transfer learning. The proposed SEI algorithm is optimized and evaluated based on real-world ACARS signals captured in the Xianyang airport. Experimental results demonstrate that our algorithm can distinguish authorized entities from unauthorized entities and obtain an identification accuracy of up to 0.980. In addition, the design rationality and the superiority over other algorithms are verified through the experiments.