An Innovative Secure and Privacy-Preserving Federated Learning Based Hybrid Deep Learning Model for Intrusion Detection in Internet-Enabled Wireless Sensor Networks

Cyberspace faces numerous security challenges, necessitating advanced research in intrusion detection systems (IDS) to mitigate vulnerabilities. Wireless Sensor Networks (WSNs) connected to the Internet are particularly vulnerable, requiring robust protection mechanisms. Traditional IDS struggle wit...

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Bibliographic Details
Published inIEEE transactions on consumer electronics p. 1
Main Authors Jena, Soumya Ranjan, Rahman, Mohammad Zia Ur, Sinha, Deepak K., kumar, P. Rajendra, Vimal, Vrince, Singh, Kamred Udham, Syamsundararao, Thalakola, Kumar, J.N.V.R. Swarup, J, Balajee
Format Journal Article
LanguageEnglish
Published IEEE 13.08.2024
Subjects
Accuracy
Computer architecture
Decision Making
Deep Learning
Feature extraction
Federated Learning
Internet of Things
Intrusion detection
Intrusion Detection System
Microprocessors
Wireless Sensor Network
Wireless sensor networks
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Summary:Cyberspace faces numerous security challenges, necessitating advanced research in intrusion detection systems (IDS) to mitigate vulnerabilities. Wireless Sensor Networks (WSNs) connected to the Internet are particularly vulnerable, requiring robust protection mechanisms. Traditional IDS struggle with identifying unknown attacks and maintaining data privacy, especially in WSNs. This study proposes a novel approach integrating Stacked Convolutional Neural Networks (SCNN), Bidirectional Long Short Term Memory (Bi-LSTM), and the African Vulture Optimization Algorithm (AVOA) within a framework of Federated Learning (FL). The integrated model, SCNN-Bi-LSTM-AVOA-FL, aims to enhance intrusion detection efficacy while preserving data privacy. A tailored AVOA optimization method fine-tunes SCNN-Bi-LSTM hyperparameters, leveraging specialized datasets (WSN-DS, CIC-IDS-2017, and WSN-BFSF) for attack detection and categorization. Evaluations compare variants with and without FL techniques (proposed-1 and proposed-2) across metrics such as accuracy, precision, recall, and F1-Score. Results demonstrate significant improvements in prediction performance, validating the efficacy of the proposed approach in enhancing IDS capabilities for WSNs. This research contributes a comprehensive framework for addressing security challenges in WSNs through advanced machine learning and optimization techniques.
ISSN:0098-3063
DOI:10.1109/TCE.2024.3442015