LAUSR

At present, the data classification based on SOA data exchange method of internet of things (IoT) data is not perfect, the effectiveness of data filtering is low, and the security of data exchange is poor. In this paper, the mass data of IoT are classified by transfer-boost method. The auxiliary training data are used to help source training data and build a reliable classifier to make the classifier more accurate in the test data. Hedge grammar is used to process massive data of heterogeneous IoT. The buffer mechanism is introduced to deal with the unstable data flow in the IoT, so as to enhance the effectiveness of data filtering, and realise the secure data exchange through modules such as server request, identity authentication and receiving data. Experimental results showed that the proposed model can improve the classification accuracy and data filtering effect, and achieve a more secure data exchange effect.