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All Journal Journal of Selvicoltura Asean Journal of Biomedical and Techno Nanomaterials Journal of Social Entrepreneurship and Creative Technology Journal of Social Science Utilizing Technology
Ethan Tan
National University of Singapore
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Decision Sciences, Operations Research & Management

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Journal : journal of social science utilizing technology

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Adaptive Defense Mechanisms: A Federated Learning Approach for Proactive Intrusion Detection in Heterogeneous IoT Networks Zainal Syahlan; Ethan Tan
Journal of Social Science Utilizing Technology Vol. 4 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v4i2.3808

Abstract

Background. The rise of heterogeneous IoT devices has increased security risks, but traditional intrusion detection systems struggle with the diversity and limited resources of these devices. Purpose. This research investigates Federated Learning (FL) to develop a decentralized, adaptive IDS that enables collaborative threat detection while ensuring data privacy and low computational load. Method. An FL model was implemented in a simulated IoT network featuring sensors and industrial controllers, then tested against DoS and data injection attacks using accuracy and resource metrics. Results. The FL-based IDS reached a detection accuracy of 95.3% with minimal resource consumption, proving its efficiency for resource-constrained IoT environments. Conclusion. Federated Learning provides a scalable and proactive solution for IoT security, offering a robust framework for privacy-preserving and efficient intrusion detection.
Co-Authors Arteurt Yoseph Merung Ava Lee Johannes Muller Lena Hoffmann Li Wei Lucas Wong Riko Kobayashi Ryan Teo Zainal Syahlan