KG-IBL: Knowledge Graph Driven Incremental Broad Learning for Few-Shot Specific Emitter Identification

Specific emitter identification (SEI) plays a crucial role in the security of the Industrial Internet of Things (IIoT). In recent years, research on applying deep learning (DL) methods for signal identification has mushroomed. However, DL-based SEI methods rely on a huge amount of training data and powerful computing devices, limiting their application scenarios. In addition, DL models are considered black box models with poor interpretability. To solve the above problems, this paper proposes a novel few-shot SEI solution using knowledge graph-driven incremental broad learning (KG-IBL). Specifically, this paper uses a deep belief network (DBN) to dig deep into features and expand the broad structure with additional enhancement nodes. Furthermore, the proposed KG-IBL does not need to retrain all data to achieve dynamic incremental update learning. To our knowledge, this is the first endeavor to integrate KG with broad learning for addressing the few-shot SEI problem. The experimental results demonstrate that the proposed KG-IBL surpasses existing incremental methods in both identification performance and computational overhead. Last but not least, the accuracy of the proposed KG-IBL is 97.5%, which is only 1.67% lower than the theoretical upper limit, and the training time is nearly 267 times lower than that of deep learning models. The code and dataset are available for download at https://github.com/Lollipophua/KG-IBL.