EconPapers    
Economics at your fingertips  
 

NIDS-FGPA: A federated learning network intrusion detection algorithm based on secure aggregation of gradient similarity models

JiaMing Wang, Kai Yang and MinJing Li

PLOS ONE, 2024, vol. 19, issue 10, 1-33

Abstract: With the rapid development of Industrial Internet of Things (IIoT), network security issues have become increasingly severe, making intrusion detection one of the key technologies for ensuring IIoT security. However, existing intrusion detection systems face challenges such as incomplete data features, missing labels, parameter leakage, and high communication overhead. To address these challenges, this paper proposes a federated learning-based intrusion detection algorithm (NIDS-FGPA) that utilizes gradient similarity model aggregation. This algorithm leverages a federated learning architecture and combines it with Paillier homomorphic encryption technology to ensure the security of the training process. Additionally, the paper introduces the Gradient Similarity Model Aggregation (GSA) algorithm, which dynamically selects and weights updates from different models to reduce communication overhead. Finally, the paper designs a deep learning model based on two-dimensional convolutional neural networks and bidirectional gated recurrent units (2DCNN-BIGRU) to handle incomplete data features and missing labels in network traffic data. Experimental validation on the Edge-IIoTset and CIC IoT 2023 datasets achieves accuracies of 94.5% and 99.2%, respectively. The results demonstrate that the NIDS-FGPA model possesses the ability to identify and capture complex network attacks, significantly enhancing the overall security of the network.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308639 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 08639&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0308639

DOI: 10.1371/journal.pone.0308639

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().

 
Page updated 2025-05-05
Handle: RePEc:plo:pone00:0308639