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:
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.
    
     
 
       

Author Related Publications

    Department Related Publications

    • Rodyna Ahmed Mahmoud, "Proximity structures and grill", ijser, 2013 More
    • Heba Ibrahim Mustafa, "On rough approximations via ideal", Elsevier, 2013 More
    • Heba Ibrahim Mustafa, "Soft Generalized Closed Sets with Respect to an Ideal in Soft Topological Spaces", Natural science publishing USA, 2014 More
    • Heba Ibrahim Mustafa, "Hybridizing Rough Sets and Double Sets (An approach for increasing decision accuracy)", Acta Zhengzhou University Overseas, 2013 More
    • Alaa Hassan Attia Hassan, "On subordination results for certain new classes of analytic functions defined by using Salagean operator", Universiteti i Prishtines, Prishtine, Kosove, 2012 More
    Tweet