Federated learning framework for IoT intrusion detection using tab transformer and nature-inspired hyperparameter optimization

Faculty Science Year: 2025
Type of Publication: ZU Hosted Pages: 1526480
Authors:
Mohamed El Sayed Ahmed Muhamed
Journal: Frontiers in Big Data Frontiers Media SA Volume: 8
Keywords : Federated learning framework , , intrusion detection using    
Abstract:
This paper introduces a federated learning framework for intrusion detection systems in IoT environments, utilizing a tab transformer enhanced with nature-inspired hyperparameter optimization. The proposed framework addresses privacy concerns through centralized model training while maintaining data privacy across distributed IoT systems. The tab transformer model is enhanced with Enhanced Exponential Fitness Optimization (EEFO) to improve detection performance and accuracy in identifying security threats in IoT networks.
    
     
 
       

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