Meta-Generalization for Multiparty Privacy Learning to Identify Anomaly Multimedia Traffic in Graynet
This work addresses anomaly detection in multimedia traffic for distributed service systems, but it appears incremental as it builds on existing multiparty privacy learning models with specific optimizations.
The paper tackled the challenge of identifying anomaly multimedia traffic in distributed systems by applying meta-generalization principles to a multiparty privacy learning model in graynet, resulting in reduced generalization error and outperforming state-of-the-art models.
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.