A Novel Network Intrusion Detection Based on Variational Long Short-Term Memory and Federated Learning Under Cloud Computing Environment

Lu  Fang

doi:10.6180/jase.202605_29(5).0022

A Novel Network Intrusion Detection Based on Variational Long Short-Term Memory and Federated Learning Under Cloud Computing Environment

Computer Science and Information Engineering

Lu FangThis email address is being protected from spambots. You need JavaScript enabled to view it.

Harbin Finance University, Harbin 150030, China

Received: August 24, 2025
Accepted: September 27, 2025
Publication Date: October 18, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202605_29(5).0022

This paper proposes a novel network intrusion detection method based on Variational Long Short-Term Memory (VLSTM) and Federated Learning (FL), aiming to effectively enhance the performance of network intrusion detection in cloud computing environments. In the cloud computing environment, network traffic data has the characteristics of being massive, complex and distributedly stored. Traditional intrusion detection methods are challenged by the issues of insufficient data privacy protection and model generalization ability. To address these problems, we first utilized the VLSTM network to extract features and model the network traffic data. VLSTM, by introducing a variational mechanism, can better capture the time series features and potential uncertainty information in the network traffic data, thereby improving the ability to identify intrusion behaviors. Then, combined with the federated learning framework, we conducted collaborative training on the data distributed across different cloud nodes, without requiring the data to be centralized in a single central node. This effectively solved the problem of data privacy protection and fully utilized the advantages of distributed data, enhancing the model’s generalization ability and detection accuracy. Experimental results show that this method outperforms the existing mainstream intrusion detection methods in key indicators such as detection rate and false alarm rate, providing a new and effective solution for network intrusion detection in the cloud computing environment, and has significant theoretical and practical value.

Keywords: Network intrusion detection, VLSTM, Attention mechanism, Cloud computing environment, Federated Learning

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