Abstract: The existing IoT device recognition methods have the problems of single feature dimension for characterizing device fingerprints,incomplete selection of traffic feature information,which easily lead to insufficient ability to characterize traffic features,and fail to fully exploit the recognition potential of multiple network models,resulting in unsatisfactory recognition results.To address these problems,this paper proposes a method called MultiDI(IoT device recognition method combining multimodal IoT device fingerprint and ensemble learning).First,to enhance the feature representation ability of IoT device fingerprints while preserving the traffic feature information,an improved Nilsimsa algorithm and data visualization method are combined to develop a multimodal IoT device fingerprint generation algorithm.Then,based on the generated IoT device fingerprint features,three neural network models are used to explore the different dimensional information of multimodal fingerprint features,enabling more comprehensive learning and recognition of IoT device traffic features.Lastly,to further explore the recognition potential of multiple network models,a classification connection network is constructed using weighted classification and LeakyRelu activation function.The proposed classification connection network is employed for ensemble learning,integrating the recognition results from multiple network models to enhance the accuracy of the MultiDI method for IoT device recognition.Experimental results show that the MultiDI method achieves 91.3%,98.6% and 99.2% weighted F1 values on the three datasets,respectively,which verifies its effectiveness.Compared with multiple IoT device recognition methods,it presents a relatively good recognition effect,verifing its efficiency.

Key words: Network traffic, Multimodal IoT device fingerprint, Ensemble learning, IoT device recognition