.png){kind=icon}
Return
Simplifying Multi-Domain Specific Emitter Identification
.png){kind=icon}
OA
AI
DOI：10.1109/ojcoms.2026.3713690.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
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:
619
Citations:
0
Organization
Cited Papers
Physical Layer-Based IoT Security: An Investigation Into Improving Preamble-Based SEI Performance When Using Multiple Waveform Collections.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSaveShareSaveShareSaveDeepRadioIDProceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing
OAAI
Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and ComputingIF0
OAAIShareSave
View more