Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning

TL;DR

This paper introduces a robust rogue emitter detection method combining denoising autoencoder and deep metric learning, significantly improving noise robustness and feature discrimination in low SNR scenarios for IoT security.

Contribution

It presents a novel hybrid network approach that enhances rogue emitter detection accuracy and robustness under challenging noisy conditions, outperforming existing methods.

Findings

Achieves higher detection accuracy in low SNR environments.

Demonstrates improved noise robustness over existing methods.

Produces more discriminative semantic vectors for emitter identification.

Abstract

Rogue emitter detection (RED) is a crucial technique to maintain secure internet of things applications. Existing deep learning-based RED methods have been proposed under the friendly environments. However, these methods perform unstable under low signal-to-noise ratio (SNR) scenarios. To address this problem, we propose a robust RED method, which is a hybrid network of denoising autoencoder and deep metric learning (DML). Specifically, denoising autoencoder is adopted to mitigate noise interference and then improve its robustness under low SNR while DML plays an important role to improve the feature discrimination. Several typical experiments are conducted to evaluate the proposed RED method on an automatic dependent surveillance-Broadcast dataset and an IEEE 802.11 dataset and also to compare it with existing RED methods. Simulation results show that the proposed method achieves better RED performance and higher noise robustness with more discriminative semantic vectors than existing methods.