论文标题
多党隐私学习的元化将识别Graynet中的多媒体流量
Meta-Generalization for Multiparty Privacy Learning to Identify Anomaly Multimedia Traffic in Graynet
论文作者
论文摘要
在网络空间中识别异常多媒体流量是分布式服务系统，多代网络和未来所有事物的互联网的巨大挑战。这封信探索了Graynet中多方隐私学习模型的荟萃化，以提高异常多媒体交通识别的性能。 Graynet中的多方隐私学习模型是一个全球共享的模型，通过与保留私有数据的多方参数更新进行了分区，分布和培训。元将军是指发现学习模型的固有属性，以减少其泛化误差。在实验中，测试了三个元将军原理如下。通过更改字节级嵌入式的维度来减少多方隐私学习模型的概括误差。随后，通过调整深度来提取数据包级特征来减少误差。最后，通过调整用于预处理流量级数据的支持设置的大小来减少错误。实验结果表明，该提案的表现优于确定多媒体流量的最新学习模型。
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.
