Open Access

ARTICLE

An Intrusion Detection Scheme Based on Federated Learning and Self-Attention Fusion Convolutional Neural Network for IoT

by Jie Deng¹, Ran Guo², Zilong Jin^1,3,*

1 School of Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, 510006, China
3 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Zilong Jin. Email:

Journal on Internet of Things 2022, 4(3), 141-153. https://doi.org/10.32604/jiot.2022.038914

Received 03 December 2022; Accepted 05 January 2023; Issue published 12 June 2023

Abstract

Traditional based deep learning intrusion detection methods face problems such as insufficient cloud storage, data privacy leaks, high communication costs, unsatisfactory detection rates, and false positive rate. To address existing issues in intrusion detection, this paper presents a novel approach called CS-FL, which combines Federated Learning and a Self-Attention Fusion Convolutional Neural Network. Federated Learning is a new distributed computing model that enables individual training of client data without uploading local data to a central server. at the same time, local training results are uploaded and integrated across all participating clients to produce a global model. The sharing model reduces communication costs, protects data privacy, and solves problems such as insufficient cloud storage and “data islands” for each client. In the proposed method, a hybrid model is formed by integrating the self-Attention and similar parts of the Convolutional Neural Network in the local data processing. This approach not only enhances the performance of the hybrid model but also reduces computational overhead compared to pure hybrid neural networks. Results from experiments on the NSL-KDD dataset show that the proposed method outperforms other intrusion detection techniques, resulting in a significant improvement in performance. This demonstrates the effectiveness of the proposed approach in improving intrusion detection accuracy.

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Keywords

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