Internet of Things (IoT) intrusion detection by Machine Learning (ML): a review
One of today's fastest-growing technologies is the Internet of Things (IoT). It is a technology that lets billions of smart devices or objects known as "Things" collect different kinds of data about themselves and their surroundings utilizing different sensors. For example, it could b...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2023
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| Online Access: | http://journalarticle.ukm.my/22536/ http://journalarticle.ukm.my/22536/1/02%20-.pdf |
| _version_ | 1848815623633633280 |
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| author | Dehkordi, Iman Farhadian Manochehri, Kooroush Aghazarian, Vahe |
| author_facet | Dehkordi, Iman Farhadian Manochehri, Kooroush Aghazarian, Vahe |
| author_sort | Dehkordi, Iman Farhadian |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | One of today's fastest-growing technologies is the Internet of Things (IoT). It is a technology that lets billions of smart devices or objects known as "Things" collect different kinds of data about themselves and their surroundings utilizing different sensors. For example, it could be used to keep an eye on and regulate industrial services, or it could be used to improve corporate operations. But the IoT currently faces more security threats than ever before. This review paper discusses the many sorts of cybersecurity attacks that may be used against IoT devices. Also, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and Artificial Neural Network (ANN) are examples of Machine Learning (ML) approaches that can be employed in IDS. The goal of this study is to show the results of analyzing various classification algorithms in terms of confusion matrix, accuracy, precision, specificity, sensitivity, and f-score to Develop an Intrusion Detection System (IDS) model. |
| first_indexed | 2025-11-15T00:52:55Z |
| format | Article |
| id | oai:generic.eprints.org:22536 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:52:55Z |
| publishDate | 2023 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:225362023-11-23T03:18:30Z http://journalarticle.ukm.my/22536/ Internet of Things (IoT) intrusion detection by Machine Learning (ML): a review Dehkordi, Iman Farhadian Manochehri, Kooroush Aghazarian, Vahe One of today's fastest-growing technologies is the Internet of Things (IoT). It is a technology that lets billions of smart devices or objects known as "Things" collect different kinds of data about themselves and their surroundings utilizing different sensors. For example, it could be used to keep an eye on and regulate industrial services, or it could be used to improve corporate operations. But the IoT currently faces more security threats than ever before. This review paper discusses the many sorts of cybersecurity attacks that may be used against IoT devices. Also, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and Artificial Neural Network (ANN) are examples of Machine Learning (ML) approaches that can be employed in IDS. The goal of this study is to show the results of analyzing various classification algorithms in terms of confusion matrix, accuracy, precision, specificity, sensitivity, and f-score to Develop an Intrusion Detection System (IDS) model. Penerbit Universiti Kebangsaan Malaysia 2023-06 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/22536/1/02%20-.pdf Dehkordi, Iman Farhadian and Manochehri, Kooroush and Aghazarian, Vahe (2023) Internet of Things (IoT) intrusion detection by Machine Learning (ML): a review. Asia-Pacific Journal of Information Technology and Multimedia, 12 (1). pp. 13-38. ISSN 2289-2192 https://www.ukm.my/apjitm/ |
| spellingShingle | Dehkordi, Iman Farhadian Manochehri, Kooroush Aghazarian, Vahe Internet of Things (IoT) intrusion detection by Machine Learning (ML): a review |
| title | Internet of Things (IoT) intrusion detection by Machine Learning (ML): a review |
| title_full | Internet of Things (IoT) intrusion detection by Machine Learning (ML): a review |
| title_fullStr | Internet of Things (IoT) intrusion detection by Machine Learning (ML): a review |
| title_full_unstemmed | Internet of Things (IoT) intrusion detection by Machine Learning (ML): a review |
| title_short | Internet of Things (IoT) intrusion detection by Machine Learning (ML): a review |
| title_sort | internet of things (iot) intrusion detection by machine learning (ml): a review |
| url | http://journalarticle.ukm.my/22536/ http://journalarticle.ukm.my/22536/ http://journalarticle.ukm.my/22536/1/02%20-.pdf |