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

TL;DR

This paper introduces a meta-generalization approach for multiparty privacy learning in graynet to enhance anomaly multimedia traffic detection, demonstrating improved performance over existing models through parameter adjustments.

Contribution

It proposes a novel meta-generalization framework for multiparty privacy learning in graynet, optimizing model parameters to reduce generalization error in anomaly traffic identification.

Findings

Meta-generalization reduces error via byte-level embedding dimension adjustment.

Adapting packet-level feature extraction depth improves model accuracy.

Tuning traffic data preprocessing enhances anomaly detection performance.

Abstract

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.