Abstract: In order to isolate the vulnerable and abnormal IoT devices in the local area network in time,efficient device classification and identification capability is very important for network administrators.The features selected in the existing methods are not highly correlated with equipment,and the sample data is unbalanced due to differences in equipment status.Aiming at the above problems,this paper proposes an IoT device classification and identification model FT-DRF based on traffic and text fingerprints.This method firstly designs a feature mining model,selects stable flow statistics as device traffic fingerprints,and then generates device text fingerprints based on sensitive text information in the header fields of application layer protocols such as HTTP,DNS,and DHCP.On this basis,the data is preprocessed and the feature vector is generated.Finally,a machine learning algorithm based on double-layer random forest is designed to classify and identify the devices.A supervised classification and re-cognition experiment is conducted on the simulated smart home environment dataset composed of 13 IoT devices and public dataset.The results show that the FT-DRF model can identify IoT devices such as network cameras and smart speakers,with an ave-rage accuracy rate of 99.81%,which is 2%~5% higher than that of the existing typical methods.

Key words: Internetof Things, Device recognition, Machine learning, Traffic classification, Sensitive text