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Simplifying Multi-Domain Specific Emitter Identification
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DOI：10.1109/ojcoms.2026.3713690.png)
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Abstract
En 中文
Wireless communications systems are a seamless, ubiquitous part of daily life and therefore require security; however, consistent, pertinent, and effective security remains an ongoing challenge. Physical-layer security mechanisms, such as Specific Emitter Identification (SEI), can complement bit-level security mechanisms and help address this challenge by providing the “something you are” to Multi-Factor Authentication’s “something you have” and “something you know”. Currently, Deep Learning (DL) based SEI is a prominent physical-layer security approach; however, it faces a fundamental challenge known as the multi-domain problem. The multi-domain problem occurs when a Neural Network (NN) trained on one dataset suffers severe performance degradation when classifying data from another dataset it did not see during training. This work introduces Authenticated Waveform-based Emitter identification via Synchronized Observation and MAC-validation Estimation (AWESOME), a multi-domain SEI approach that addresses this performance degradation by mitigating waveform-collection artifacts and procedures while simultaneously reducing NN architecture complexity. This results in a computationally efficient methodology that generalizes well across emitter types and wireless protocols while outperforming more complex domain adaptation techniques. The presented multi-domain SEI approach uses a single-layer Long Short-Term Memory (LSTM) to achieve accuracies up to 99.37% when identifying eight Wireless-Fidelity (Wi-Fi) emitters and 98.1% when identifying eight ZigBee emitters with single waveform decisions made in approximately 49.39 $\mu $ s and 116.94 $\mu $ s, respectively.
Keywords:
Artificial intelligence
communications security
cross collection
deep learning
domain adaptation
multi-domain
physical-layer security
RF fingerprinting
specific emitter identification
Journal
I
IF:
6.1
Papers:
489
Citations:
0