Identifying anomaly multimedia traffic in cyberspace is a big challenge in
distributed service systems, multiple generation networks and future internet
of everything. This letter explores meta-generalization for a multiparty
privacy learning model in graynet to improve the performance of anomaly
multimedia traffic identification. The multiparty privacy learning model in
graynet is a globally shared model that is partitioned, distributed and trained
by exchanging multiparty parameters updates with preserving private data. The
meta-generalization refers to discovering the inherent attributes of a learning
model to reduce its generalization error. In experiments, three
meta-generalization principles are tested as follows. The generalization error
of the multiparty privacy learning model in graynet is reduced by changing the
dimension of byte-level imbedding. Following that, the error is reduced by
adapting the depth for extracting packet-level features. Finally, the error is
reduced by adjusting the size of support set for preprocessing traffic-level
data. Experimental results demonstrate that the proposal outperforms the
state-of-the-art learning models for identifying anomaly multimedia traffic.Comment: Correct some typo