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Identifying anomaly multimedia traffic in cyberspace is a big challenge indistributed service systems, multiple generation networks and future internetof everything. This letter explores meta-generalization for a multipartyprivacy learning model in graynet to improve the performance of anomalymultimedia traffic identification. The multiparty privacy learning model ingraynet is a globally shared model that is partitioned, distributed and trainedby exchanging multiparty parameters updates with preserving private data. Themeta-generalization refers to discovering the inherent attributes of a learningmodel to reduce its generalization error. In experiments, threemeta-generalization principles are tested as follows. The generalization errorof the multiparty privacy learning model in graynet is reduced by changing thedimension of byte-level imbedding. Following that, the error is reduced byadapting the depth for extracting packet-level features. Finally, the error isreduced by adjusting the size of support set for preprocessing traffic-leveldata. Experimental results demonstrate that the proposal outperforms thestate-of-the-art learning models for identifying anomaly multimedia traffic.

Meta-Generalization for Multiparty Privacy Learning to Identify Anomaly Multimedia Traffic in Graynet