Abstract: In order to ensure the security management of the hospital information network and avoid medical information leakage, an intrusion detection algorithm for abnormal information in the hospital network based on deep generative model was proposed. Using binary wavelet transform method, multi-scale decomposition of hospital network operation data, combined with adaptive soft threshold denoising coefficient to extract effective data. The Wasserstein distance algorithm and MMD(Maximun Mean Discrepancy) distance algorithm in the optimal transportation theory are used to reduce the dimension of the hospital network data in the depth generative model, input the reduced dimension network normal operation data samples into the anomaly detection model, and extract the sample characteristics. Using the Adam algorithm in deep learning strategy, generate an anomaly information discrimination function, and compare the characteristics of the tested network operation data with the normal network operation data to achieve hospital network anomaly information intrusion detection. The experimental results show that the algorithm can achieve efficient detection of abnormal information intrusion in hospital networks, accurately detect multiple types of network intrusion behaviors, and provide security guarantees for the network operation of medical institutions. 

Key words: binary wavelet transform, depth generative model, wasserstein distance algorithm, maximun mean discrepancy(MMD) distance algorithm, hospital network, abnormal information, intrusion detection