Overcoming the challenges of data lack, leakage, and dimensionality in intrusion detection systems: a comprehensive review
The Internet of Things (IoT) and cloud computing are rapidly gaining momentum as decentralized Internet-based technologies, leading to increased information in nearly every technical and commercial industry. However, ensuring the security of IoT systems is a pressing issue due to the complexities in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Brazilian Telecommunications Society
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/116409/ http://psasir.upm.edu.my/id/eprint/116409/1/116409.pdf |
| _version_ | 1848866998326394880 |
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| author | Abdullah, Azizol Bouke, Mohamed Udzir, Nur Samian, Normalia |
| author_facet | Abdullah, Azizol Bouke, Mohamed Udzir, Nur Samian, Normalia |
| author_sort | Abdullah, Azizol |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The Internet of Things (IoT) and cloud computing are rapidly gaining momentum as decentralized Internet-based technologies, leading to increased information in nearly every technical and commercial industry. However, ensuring the security of IoT systems is a pressing issue due to the complexities involved in connected and shared environments. Networks are guarded by Intrusion Detection Systems (IDS) against various cyber threats such as malware, viruses, and unauthorized access. IDS has recently adopted Machine Learning (ML) and Deep Learning (DL) techniques to identify and classify security risks. However, the effective utilization of these technologies depends on the availability, quality, and characteristics of the data used to train models. Moreover, data lack, data leak, and dimensionality (DLLD) are common problems in data science and ML. This paper surveys existing research and suggests solutions for overcoming DLLD-related issues to improve the IDS model. |
| first_indexed | 2025-11-15T14:29:30Z |
| format | Article |
| id | upm-116409 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:29:30Z |
| publishDate | 2024 |
| publisher | Brazilian Telecommunications Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1164092025-04-07T06:12:40Z http://psasir.upm.edu.my/id/eprint/116409/ Overcoming the challenges of data lack, leakage, and dimensionality in intrusion detection systems: a comprehensive review Abdullah, Azizol Bouke, Mohamed Udzir, Nur Samian, Normalia The Internet of Things (IoT) and cloud computing are rapidly gaining momentum as decentralized Internet-based technologies, leading to increased information in nearly every technical and commercial industry. However, ensuring the security of IoT systems is a pressing issue due to the complexities involved in connected and shared environments. Networks are guarded by Intrusion Detection Systems (IDS) against various cyber threats such as malware, viruses, and unauthorized access. IDS has recently adopted Machine Learning (ML) and Deep Learning (DL) techniques to identify and classify security risks. However, the effective utilization of these technologies depends on the availability, quality, and characteristics of the data used to train models. Moreover, data lack, data leak, and dimensionality (DLLD) are common problems in data science and ML. This paper surveys existing research and suggests solutions for overcoming DLLD-related issues to improve the IDS model. Brazilian Telecommunications Society 2024-01-30 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/116409/1/116409.pdf Abdullah, Azizol and Bouke, Mohamed and Udzir, Nur and Samian, Normalia (2024) Overcoming the challenges of data lack, leakage, and dimensionality in intrusion detection systems: a comprehensive review. Journal of Communication and Information Systems (JCIS), 39 (1). pp. 22-34. ISSN 1980-6612; eISSN: 1980-6604 https://jcis.sbrt.org.br/jcis/article/view/862 10.14209/jcis.2024.3 |
| spellingShingle | Abdullah, Azizol Bouke, Mohamed Udzir, Nur Samian, Normalia Overcoming the challenges of data lack, leakage, and dimensionality in intrusion detection systems: a comprehensive review |
| title | Overcoming the challenges of data lack, leakage, and dimensionality in intrusion detection systems: a comprehensive review |
| title_full | Overcoming the challenges of data lack, leakage, and dimensionality in intrusion detection systems: a comprehensive review |
| title_fullStr | Overcoming the challenges of data lack, leakage, and dimensionality in intrusion detection systems: a comprehensive review |
| title_full_unstemmed | Overcoming the challenges of data lack, leakage, and dimensionality in intrusion detection systems: a comprehensive review |
| title_short | Overcoming the challenges of data lack, leakage, and dimensionality in intrusion detection systems: a comprehensive review |
| title_sort | overcoming the challenges of data lack, leakage, and dimensionality in intrusion detection systems: a comprehensive review |
| url | http://psasir.upm.edu.my/id/eprint/116409/ http://psasir.upm.edu.my/id/eprint/116409/ http://psasir.upm.edu.my/id/eprint/116409/ http://psasir.upm.edu.my/id/eprint/116409/1/116409.pdf |