Metageneralization For Multiparty Privacy Learning To Identify Anomaly Multimedia Traffic In Graynet

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

**arXiv ID:** 2201.03027
**Authors:** Satoshi Nato, Yiqiang Sheng
**Published:** 2022-01-09T14:51:34Z
**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.

## Skill Description

This skill is generated from the arXiv paper: Meta-Generalization for Multiparty Privacy Learning to Identify Anomaly Multimedia Traffic in Graynet (2201.03027).

## How to Use

[To be filled in by the user or by future automation]

## References

- [arXiv:2201.03027](http://arxiv.org/abs/2201.03027v2)