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.

本文研究了在分布式服务系统、多代网络和未来的万物互联环境下，识别异常多媒体流量的挑战，探索了基于元泛化的多方隐私学习模型在Graynet中的应用，通过改变字节级嵌入的维度、改变提取包级特征的深度、调整预处理流量级数据的支持集大小等三个元泛化原则来降低该多方隐私学习模型的泛化误差，实验结果表明该方法具有较高的性能。

Graynet中多方隐私学习的Meta-Generalization用于识别异常多媒体流量