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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS) Jurnal Manajemen Informatika Jayakarta Journal of Internet and Software Engineering Jurnal Informatika: Jurnal Pengembangan IT
Divi Galih Prasetyo Putri
Institut Teknologi Sepuluh Nopember
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Health Professions Materials Science & Nanotechnology

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

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Contract-based federated learning framework for intrusion detection system in internet of things networks Saputra, Yuris Mulya; Putri, Divi Galih Prasetyo; Putra, Jimmy Trio; Murti, Budi Bayu; Wahyono, Wahyono
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3324-3333

Abstract

A plethora of national vital infrastructures connected to internet of things (IoT) networks may trigger serious data security vulnerabilities. To address the issue, intrusion detection systems (IDS) were investigated where the behavior and traffic of IoT networks are monitored to determine whether malicious attacks or not occur through centralized learning on a cloud. Nonetheless, such a method requires IoT devices to transmit their local network traffic data to the cloud, thereby leading to data breaches. This paper proposes a federated learning (FL)-based IDS on IoT networks aiming at improving the intrusion detection accuracy without privacy leakage from the IoT devices. Specifically, an IoT service provider can first motivate IoT devices to participate in the FL process via a contract-based incentive mechanism according to their local data. Then, the FL process is executed to predict IoT network traffic types without sending IoT devices’ local data to the cloud. Here, each IoT device performs the learning process locally and only sends the trained model to the cloud for the model update. The proposed FL-based system achieves a higher utility (up to 44%) than that of a non-contract-based incentive mechanism and a higher prediction accuracy (up to 3%) than that of the local learning method using a real-world IoT network traffic dataset.
Co-Authors Afidah, Azza Ulil Alfian, Ganjar Ananda, Shafira Putri Budi Bayu Murti Dharmawan, Edeline Felicia Jasmin, Gabriela Anggerita Kusnady, Rasyid Muhammad Fakhrurrifqi Nouruzzaman, Radivan Alan Pinandhita, Pudyasta Satria Pratomo, Dinar Nugroho Putra, Jimmy Trio Putri, Salmania Jesamine Saputra, Yuris Mulya Savitri Citra Budi Wahyono Wahyono Widhy Hayuhardhika Nugraha Putra Wijayanti, Risti Yusuf, Muhammad Abyan Farras Zhafarizza, Ghifari Nafhan Muhammad