Open Access

ARTICLE

Privacy-Aware Federated Learning Framework for IoT Security Using Chameleon Swarm Optimization and Self-Attentive Variational Autoencoder

Saad Alahmari^1,*, Abdulwhab Alkharashi²

1 Department of Computer Science, Applied College, Northern Border University, Arar, 91431, Saudi Arabia
2 Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Riyadh, 11673, Saudi Arabia

* Corresponding Author: Saad Alahmari. Email:

(This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)

Computer Modeling in Engineering & Sciences 2025, 143(1), 849-873. https://doi.org/10.32604/cmes.2025.062549

Received 20 December 2024; Accepted 27 February 2025; Issue published 11 April 2025

Abstract

The Internet of Things (IoT) is emerging as an innovative phenomenon concerned with the development of numerous vital applications. With the development of IoT devices, huge amounts of information, including users’ private data, are generated. IoT systems face major security and data privacy challenges owing to their integral features such as scalability, resource constraints, and heterogeneity. These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data, creating an attractive opportunity for cyberattacks. To address these challenges, artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL), are utilized to build an intrusion detection system (IDS) that helps to secure IoT systems. Federated learning (FL) is a decentralized technique that can help to improve information privacy and performance by training the IDS on discrete linked devices. FL delivers an effectual tool to defend user confidentiality, mainly in the field of IoT, where IoT devices often obtain privacy-sensitive personal data. This study develops a Privacy-Enhanced Federated Learning for Intrusion Detection using the Chameleon Swarm Algorithm and Artificial Intelligence (PEFLID-CSAAI) technique. The main aim of the PEFLID-CSAAI method is to recognize the existence of attack behavior in IoT networks. First, the PEFLID-CSAAI technique involves data preprocessing using Z-score normalization to transform the input data into a beneficial format. Then, the PEFLID-CSAAI method uses the Osprey Optimization Algorithm (OOA) for the feature selection (FS) model. For the classification of intrusion detection attacks, the Self-Attentive Variational Autoencoder (SA-VAE) technique can be exploited. Finally, the Chameleon Swarm Algorithm (CSA) is applied for the hyperparameter fine-tuning process that is involved in the SA-VAE model. A wide range of experiments were conducted to validate the execution of the PEFLID-CSAAI model. The simulated outcomes demonstrated that the PEFLID-CSAAI technique outperformed other recent models, highlighting its potential as a valuable tool for future applications in healthcare devices and small engineering systems.

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