A secure edge computing model using machine learning and IDS to detect and isolate intruders
The article presents a secure edge computing model that utilizes machine learning for intrusion detection and isolation. It addresses the security challenges arising from the rapid expansion of IoT and edge computing. The proposed Intrusion Detection System (IDS) combines Linear Discriminant Analysi...
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Elsevier B.V.
2025
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| _version_ | 1833411403100192768 |
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| author | Mahadevappa P. Murugesan R.K. Al-amri R. Thabit R. Al-Ghushami A.H. Alkawsi G. |
| author2 | 57222119090 |
| author_facet | 57222119090 Mahadevappa P. Murugesan R.K. Al-amri R. Thabit R. Al-Ghushami A.H. Alkawsi G. |
| author_sort | Mahadevappa P. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The article presents a secure edge computing model that utilizes machine learning for intrusion detection and isolation. It addresses the security challenges arising from the rapid expansion of IoT and edge computing. The proposed Intrusion Detection System (IDS) combines Linear Discriminant Analysis (LDA) and Logistic Regression (LR) to swiftly and accurately identify intrusions without alerting neighboring devices. The model outperforms existing solutions with an accuracy of 96.56%, precision of 95.78%, and quick training time (0.04 s). It is effective against various types of attacks, enhancing the security of edge networks for IoT applications. ? The methodology employs a hybrid model that combines LDA and LR for intrusion detection. ? Machine learning techniques are used to analyze and identify intrusive activities during data acquisition by edge nodes. ? The methodology includes a mechanism to isolate suspected devices and data without notifying neighboring edge nodes to prevent intruders from gaining control over the edge network. ? 2024 The Author(s) |
| format | Article |
| id | my.uniten.dspace-36674 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | my.uniten.dspace-366742025-03-03T15:43:49Z A secure edge computing model using machine learning and IDS to detect and isolate intruders Mahadevappa P. Murugesan R.K. Al-amri R. Thabit R. Al-Ghushami A.H. Alkawsi G. 57222119090 57198406478 57224896623 58891173100 57202984923 57191982354 adult article diagnostic test accuracy study discriminant analysis human learning logistic regression analysis machine learning major clinical study male The article presents a secure edge computing model that utilizes machine learning for intrusion detection and isolation. It addresses the security challenges arising from the rapid expansion of IoT and edge computing. The proposed Intrusion Detection System (IDS) combines Linear Discriminant Analysis (LDA) and Logistic Regression (LR) to swiftly and accurately identify intrusions without alerting neighboring devices. The model outperforms existing solutions with an accuracy of 96.56%, precision of 95.78%, and quick training time (0.04 s). It is effective against various types of attacks, enhancing the security of edge networks for IoT applications. ? The methodology employs a hybrid model that combines LDA and LR for intrusion detection. ? Machine learning techniques are used to analyze and identify intrusive activities during data acquisition by edge nodes. ? The methodology includes a mechanism to isolate suspected devices and data without notifying neighboring edge nodes to prevent intruders from gaining control over the edge network. ? 2024 The Author(s) Final 2025-03-03T07:43:49Z 2025-03-03T07:43:49Z 2024 Article 10.1016/j.mex.2024.102597 2-s2.0-85185300096 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185300096&doi=10.1016%2fj.mex.2024.102597&partnerID=40&md5=c8231d5a145f9de7447796e6592ce70f https://irepository.uniten.edu.my/handle/123456789/36674 12 102597 All Open Access; Green Open Access Elsevier B.V. Scopus |
| spellingShingle | adult article diagnostic test accuracy study discriminant analysis human learning logistic regression analysis machine learning major clinical study male Mahadevappa P. Murugesan R.K. Al-amri R. Thabit R. Al-Ghushami A.H. Alkawsi G. A secure edge computing model using machine learning and IDS to detect and isolate intruders |
| title | A secure edge computing model using machine learning and IDS to detect and isolate intruders |
| title_full | A secure edge computing model using machine learning and IDS to detect and isolate intruders |
| title_fullStr | A secure edge computing model using machine learning and IDS to detect and isolate intruders |
| title_full_unstemmed | A secure edge computing model using machine learning and IDS to detect and isolate intruders |
| title_short | A secure edge computing model using machine learning and IDS to detect and isolate intruders |
| title_sort | secure edge computing model using machine learning and ids to detect and isolate intruders |
| topic | adult article diagnostic test accuracy study discriminant analysis human learning logistic regression analysis machine learning major clinical study male |
| url_provider | http://dspace.uniten.edu.my/ |