Abstract: To ensure the security of cross domain data flow in the Internet of Things, a security detection algorithm for cross domain data flow sharing the same frequency in the Internet of Things is proposed. This method calculates the information entropy of the data set based on the data outlier characteristics, takes the data points with larger information entropy calculation results as the cluster center, and analyzes the data distribution characteristics through the calculation of the cluster center distance. The data distribution features are inputted into the BP( Back Propagation) neural network and combined with genetic learning algorithms to achieve deep mining of shared cross domain data on the same frequency. Wavelet analysis is used to segment effective signals and noise signals in the same frequency shared data, and introduce the wrcoef function achieving the reconstruction output of noise free signals. Based on the Markov chain state transition probability matrix, a detection model of Markov cross domain data flow security is established. By calculating the relative entropy difference value between the test sample and the standard sample, the security detection of cross domain data flow for the same frequency sharing of the Internet of Things is completed. The simulation results show that this method can effectively improve the efficiency of data flow security detection and achieve accurate perception of data flow trends across domains.

Key words: information entropy algorithm, genetic learning algorithm, deep data mining, wavelet analysis method, Markov model