Federated Deep Payload Classification for Industrial Internet with Cloud-Edge Architecture

Abstract

Payload classification is a kind of powerful deep packet inspection model built on the raw payloads of network traffic, and hence can remove the need of any configuration assumptions for network management and intrusion detection. While in the emerging industrial Internet, a majority of local industry owners are not willing to share their private payloads that possibly contain sensitive information and thus cause the classification model not always well trained due to the lack of sufficient training samples. In this paper, we address this privacy concern and propose a federated learning model for industrial payload classification. In particular, we consider a cloud-edge architecture for the industrial Internet topology, and assemble federated learning process by cloud-edge collaboration: each data owner has his own edge server for learning a local classification model and the industrial cloud takes the responsibility for aggregating local models to a federated one. We adopt a gradient-based deep convolutional neural network model as our local classifier and use the method of weighted gradient averaging for model aggregation. By this way, the data owners can avoid to disclose their private payload for model training, but instead share their local model’s gradients to keep the federated model able to learn local samples indirectly. At the end, we have conducted a large set of experiments with real-world industrial Internet traffic datasets, and have successfully confirmed the effectiveness of the proposed federated model for payload classification with privacy-preserved.