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Contract-based federated learning framework for intrusion detection system in internet of things networks

IAES International Journal of Artificial Intelligence · Vol. 14 No. 4 (2025) · hlm. 3324–3333

DOI
10.11591/ijai.v14.i4.pp3324-3333
ISSN
2252-8938, 2089-4872
Terbit
2025-08-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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  • Universitas Gadjah Mada
  • Institut Teknologi Sepuluh Nopember
  • University of Ulsan
  • University of Bengkulu
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Rekan penulisOrganisasiArtikel bersama
Muhammad Dzulfikar FauziUniversitas Gadjah Mada2
Chasandra PuspitasariUniversitas Gadjah Mada1
Kasliono KaslionoUniversitas Gadjah Mada1
Wahyu Sri MulyaniUniversitas Gadjah Mada1
Laksono KurnianggoroUniversity of Ulsan1
Joko HariyonoAfiliasi tidak diketahui1
Agus HarjokoUniversitas Gadjah Mada1
Abdul KholikUniversitas Gadjah Mada1
Suprapto SupraptoUniversitas Gadjah Mada1
Yaqutina Marjani SantosaUniversitas Gadjah Mada1
Agfianto Eko PutraUniversitas Gadjah Mada1
Nur RokhmanUniversitas Gadjah Mada1
Riska Amalia PraptiwiUniversitas Gadjah Mada1
Aliyyah Nur AzhariUniversitas Gadjah Mada1
Siti RochimahInstitut Teknologi Sepuluh Nopember1
Muhammad Shulhan KhairyInstitut Teknologi Sepuluh Nopember1
Fahmizal FahmizalUniversitas Gadjah Mada1
Afrizal MayubUniversity of Bengkulu1
Tommy Richard OrlandoUniversitas Gadjah Mada1
Maun BudiyantoUniversitas Gadjah Mada1
Unan Yusmaniar OktiawatiUniversitas Gadjah Mada1
Nur Rohman RosyidUniversitas Gadjah Mada1
Ardhi Wicaksono SantosoUniversitas Gadjah Mada1
Anni Karimatul FauziyyahUniversitas Gadjah Mada1
Alif SubardonoUniversitas Gadjah Mada1
Ronald AdriánUniversitas Gadjah Mada1
Tika Erna PutriUniversitas Gadjah Mada1
Muryati MuryatiAfiliasi tidak diketahui1
Y. Andi TrisyonoUniversitas Gadjah Mada1
Witjaksono WitjaksonoUniversitas Gadjah Mada1
Savitri Citra BudiUniversitas Gadjah Mada1
Adlan Bagus PradanaUniversitas Gadjah Mada1
Muhammad Andika PratamaUniversitas Gadjah Mada1
Nur Risya Difa Kusuma NingrumUniversitas Gadjah Mada1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Contract-based federated learning framework for intrusion detection system in internet of things networks</dc:title>
	<dc:creator>Saputra, Yuris Mulya</dc:creator>
	<dc:creator>Putri, Divi Galih Prasetyo</dc:creator>
	<dc:creator>Putra, Jimmy Trio</dc:creator>
	<dc:creator>Murti, Budi Bayu</dc:creator>
	<dc:creator>Wahyono, Wahyono</dc:creator>
	<dc:subject xml:lang="en-US">Deep Learning; Federated Learning; Internet of Things</dc:subject>
	<dc:subject xml:lang="en-US">Contract theory; Federated learning; Incentive mechanism; Internet of things; Network security</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Universitas Gadjah Mada</dc:contributor>
	<dc:date>2025-08-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/27358</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i4.pp3324-3333</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 4: August 2025; 3324-3333</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/27358/14668</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>