A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks

The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks eventually. The rapidly growing Io...
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Gespeichert in:

Autor*in:	Md Mamunur Rashid [verfasserIn] Shahriar Usman Khan [verfasserIn] Fariha Eusufzai [verfasserIn] Md. Azharuddin Redwan [verfasserIn] Saifur Rahman Sabuj [verfasserIn] Mahmoud Elsharief [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	federated learning intrusion detection Internet of Things machine learning neural networks privacy

Übergeordnetes Werk:	In: Network - MDPI AG, 2021, 3(2023), 1, Seite 158-179
Übergeordnetes Werk:	volume:3 ; year:2023 ; number:1 ; pages:158-179

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/network3010008

Katalog-ID:	DOAJ087275988

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520			\|a The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks eventually. The rapidly growing IoT-connected devices under a centralized ML system could threaten data privacy. The popular centralized machine learning (ML)-assisted approaches are difficult to apply due to their requirement of enormous amounts of data in a central entity. Owing to the growing distribution of data over numerous networks of connected devices, decentralized ML solutions are needed. In this paper, we propose a Federated Learning (FL) method for detecting unwanted intrusions to guarantee the protection of IoT networks. This method ensures privacy and security by federated training of local IoT device data. Local IoT clients share only parameter updates with a central global server, which aggregates them and distributes an improved detection algorithm. After each round of FL training, each of the IoT clients receives an updated model from the global server and trains their local dataset, where IoT devices can keep their own privacy intact while optimizing the overall model. To evaluate the efficiency of the proposed method, we conducted exhaustive experiments on a new dataset named Edge-IIoTset. The performance evaluation demonstrates the reliability and effectiveness of the proposed intrusion detection model by achieving an accuracy (92.49%) close to that offered by the conventional centralized ML models’ accuracy (93.92%) using the FL method.
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callnumber-first	T - Technology
author	Md Mamunur Rashid
spellingShingle	Md Mamunur Rashid misc TK7885-7895 misc QA75.5-76.95 misc federated learning misc intrusion detection misc Internet of Things misc machine learning misc neural networks misc privacy misc Computer engineering. Computer hardware misc Electronic computers. Computer science A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks
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topic_title	TK7885-7895 QA75.5-76.95 A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks federated learning intrusion detection Internet of Things machine learning neural networks privacy
topic	misc TK7885-7895 misc QA75.5-76.95 misc federated learning misc intrusion detection misc Internet of Things misc machine learning misc neural networks misc privacy misc Computer engineering. Computer hardware misc Electronic computers. Computer science
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title	A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks
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title_sort	federated learning-based approach for improving intrusion detection in industrial internet of things networks
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title_auth	A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks
abstract	The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks eventually. The rapidly growing IoT-connected devices under a centralized ML system could threaten data privacy. The popular centralized machine learning (ML)-assisted approaches are difficult to apply due to their requirement of enormous amounts of data in a central entity. Owing to the growing distribution of data over numerous networks of connected devices, decentralized ML solutions are needed. In this paper, we propose a Federated Learning (FL) method for detecting unwanted intrusions to guarantee the protection of IoT networks. This method ensures privacy and security by federated training of local IoT device data. Local IoT clients share only parameter updates with a central global server, which aggregates them and distributes an improved detection algorithm. After each round of FL training, each of the IoT clients receives an updated model from the global server and trains their local dataset, where IoT devices can keep their own privacy intact while optimizing the overall model. To evaluate the efficiency of the proposed method, we conducted exhaustive experiments on a new dataset named Edge-IIoTset. The performance evaluation demonstrates the reliability and effectiveness of the proposed intrusion detection model by achieving an accuracy (92.49%) close to that offered by the conventional centralized ML models’ accuracy (93.92%) using the FL method.
abstractGer	The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks eventually. The rapidly growing IoT-connected devices under a centralized ML system could threaten data privacy. The popular centralized machine learning (ML)-assisted approaches are difficult to apply due to their requirement of enormous amounts of data in a central entity. Owing to the growing distribution of data over numerous networks of connected devices, decentralized ML solutions are needed. In this paper, we propose a Federated Learning (FL) method for detecting unwanted intrusions to guarantee the protection of IoT networks. This method ensures privacy and security by federated training of local IoT device data. Local IoT clients share only parameter updates with a central global server, which aggregates them and distributes an improved detection algorithm. After each round of FL training, each of the IoT clients receives an updated model from the global server and trains their local dataset, where IoT devices can keep their own privacy intact while optimizing the overall model. To evaluate the efficiency of the proposed method, we conducted exhaustive experiments on a new dataset named Edge-IIoTset. The performance evaluation demonstrates the reliability and effectiveness of the proposed intrusion detection model by achieving an accuracy (92.49%) close to that offered by the conventional centralized ML models’ accuracy (93.92%) using the FL method.
abstract_unstemmed	The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks eventually. The rapidly growing IoT-connected devices under a centralized ML system could threaten data privacy. The popular centralized machine learning (ML)-assisted approaches are difficult to apply due to their requirement of enormous amounts of data in a central entity. Owing to the growing distribution of data over numerous networks of connected devices, decentralized ML solutions are needed. In this paper, we propose a Federated Learning (FL) method for detecting unwanted intrusions to guarantee the protection of IoT networks. This method ensures privacy and security by federated training of local IoT device data. Local IoT clients share only parameter updates with a central global server, which aggregates them and distributes an improved detection algorithm. After each round of FL training, each of the IoT clients receives an updated model from the global server and trains their local dataset, where IoT devices can keep their own privacy intact while optimizing the overall model. To evaluate the efficiency of the proposed method, we conducted exhaustive experiments on a new dataset named Edge-IIoTset. The performance evaluation demonstrates the reliability and effectiveness of the proposed intrusion detection model by achieving an accuracy (92.49%) close to that offered by the conventional centralized ML models’ accuracy (93.92%) using the FL method.
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title_short	A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks
url	https://doi.org/10.3390/network3010008 https://doaj.org/article/8eab12c10aef44828e93bc145f8471da https://www.mdpi.com/2673-8732/3/1/8 https://doaj.org/toc/2673-8732
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author2	Shahriar Usman Khan Fariha Eusufzai Md. Azharuddin Redwan Saifur Rahman Sabuj Mahmoud Elsharief
author2Str	Shahriar Usman Khan Fariha Eusufzai Md. Azharuddin Redwan Saifur Rahman Sabuj Mahmoud Elsharief
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up_date	2024-07-04T00:59:46.013Z
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A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks

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