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1. A Lightweight Specific Emitter
Identification Model for IIoT Devices
Based on Adaptive Broad Learning
Abstract
Specific emitter identification (SEI) is a technology that extracts subtle
features from signals sent by emitters to identify different
effectively improve the security of the Industrial Internet of Things (IIoT) by
acting on the physical layer of the internet. Recent research on SEI has
focused on deep learning (DL) models that can automatically learn effective
inherent emitter features from raw signals. Nevertheless, training popular DL
models is computationally expensive because of the numerous
hyperparameters and nonscalable structures. This limits the application of DL
based SEI models in certain practical IIoT scen
we propose an adaptive broad learning (ABL) method to build a lightweight
SEI model. In the proposed model, the raw signal samples are mapped to
feature nodes, and the emitters are denoted as the output nodes. The hidden
nodes are directly connected to the output nodes by a broad network.
A Lightweight Specific Emitter
Identification Model for IIoT Devices
Based on Adaptive Broad Learning
Specific emitter identification (SEI) is a technology that extracts subtle
features from signals sent by emitters to identify different individuals. It can
effectively improve the security of the Industrial Internet of Things (IIoT) by
acting on the physical layer of the internet. Recent research on SEI has
focused on deep learning (DL) models that can automatically learn effective
t emitter features from raw signals. Nevertheless, training popular DL
models is computationally expensive because of the numerous
hyperparameters and nonscalable structures. This limits the application of DL
based SEI models in certain practical IIoT scenarios. To address this concern,
we propose an adaptive broad learning (ABL) method to build a lightweight
SEI model. In the proposed model, the raw signal samples are mapped to
feature nodes, and the emitters are denoted as the output nodes. The hidden
es are directly connected to the output nodes by a broad network.
Identification Model for IIoT Devices
Based on Adaptive Broad Learning
Specific emitter identification (SEI) is a technology that extracts subtle
individuals. It can
effectively improve the security of the Industrial Internet of Things (IIoT) by
acting on the physical layer of the internet. Recent research on SEI has
focused on deep learning (DL) models that can automatically learn effective
t emitter features from raw signals. Nevertheless, training popular DL
models is computationally expensive because of the numerous
hyperparameters and nonscalable structures. This limits the application of DL-
arios. To address this concern,
we propose an adaptive broad learning (ABL) method to build a lightweight
SEI model. In the proposed model, the raw signal samples are mapped to
feature nodes, and the emitters are denoted as the output nodes. The hidden
es are directly connected to the output nodes by a broad network.

2. Through this flat structure, the size and calculation amount of the model can
be effectively reduced. To further economize the computational cost, we
designed an adaptive node expansion strategy for rapidly obtaining the
optimal hyperparameters of the models. The results of experiments on real-
world data prove the superiority of ABL over popular state-of-the-art DL-based
SEI models.