Toward Improved Machine Learning-Based Intrusion Detection for Internet of Things Traffic Article Swipe
Sarah Alkadi
,
Saad Al-Ahmadi
,
Mohamed Maher Ben Ismail
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/computers12080148
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/computers12080148
The rapid development of Internet of Things (IoT) networks has revealed multiple security issues. On the other hand, machine learning (ML) has proven its efficiency in building intrusion detection systems (IDSs) intended to reinforce the security of IoT networks. In fact, the successful design and implementation of such techniques require the use of effective methods in terms of data and model quality. This paper encloses an empirical impact analysis for the latter in the context of a multi-class classification scenario. A series of experiments were conducted using six ML models, along with four benchmarking datasets, including UNSW-NB15, BOT-IoT, ToN-IoT, and Edge-IIoT. The proposed framework investigates the marginal benefit of employing data pre-processing and model configurations considering IoT limitations. In fact, the empirical findings indicate that the accuracy of ML-based IDS detection rapidly increases when methods that use quality data and models are deployed. Specifically, data cleaning, transformation, normalization, and dimensionality reduction, along with model parameter tuning, exhibit significant potential to minimize computational complexity and yield better performance. In addition, MLP- and clustering-based algorithms outperformed the remaining models, and the obtained accuracy reached up to 99.97%. One should note that the performance of the challenger models was assessed using similar test sets, and this was compared to the results achieved using the relevant pieces of research.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/computers12080148
- https://www.mdpi.com/2073-431X/12/8/148/pdf?version=1690445934
- OA Status
- gold
- Cited By
- 18
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385345588
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385345588Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/computers12080148Digital Object Identifier
- Title
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Toward Improved Machine Learning-Based Intrusion Detection for Internet of Things TrafficWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-27Full publication date if available
- Authors
-
Sarah Alkadi, Saad Al-Ahmadi, Mohamed Maher Ben IsmailList of authors in order
- Landing page
-
https://doi.org/10.3390/computers12080148Publisher landing page
- PDF URL
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https://www.mdpi.com/2073-431X/12/8/148/pdf?version=1690445934Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2073-431X/12/8/148/pdf?version=1690445934Direct OA link when available
- Concepts
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Computer science, Cluster analysis, Machine learning, Normalization (sociology), Intrusion detection system, Artificial intelligence, Benchmarking, Context (archaeology), Data mining, Boosting (machine learning), Dimensionality reduction, Anthropology, Paleontology, Biology, Business, Sociology, MarketingTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 10, 2023: 1Per-year citation counts (last 5 years)
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65Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2762644836, https://openalex.org/W2962910311, https://openalex.org/W2590373591, https://openalex.org/W2941219224, https://openalex.org/W2791951418, https://openalex.org/W2805786304, https://openalex.org/W2608418534, https://openalex.org/W2335999708, https://openalex.org/W2560185016, https://openalex.org/W2939746199, https://openalex.org/W4297152848, https://openalex.org/W2769077153, https://openalex.org/W4366438776, https://openalex.org/W4366415335, https://openalex.org/W4312240912, https://openalex.org/W4353046955, https://openalex.org/W2963748489, https://openalex.org/W2934386458, https://openalex.org/W3085955590, https://openalex.org/W3106741970, https://openalex.org/W3195743518, https://openalex.org/W3128756376, https://openalex.org/W4297846671, https://openalex.org/W4226319939, https://openalex.org/W3196743383, https://openalex.org/W4319264752, https://openalex.org/W2334853001, https://openalex.org/W3206599325, https://openalex.org/W3144134378, https://openalex.org/W3031222267, https://openalex.org/W2342408547, https://openalex.org/W6810253496, https://openalex.org/W6682981795, https://openalex.org/W4376611545, https://openalex.org/W3104128335, https://openalex.org/W6689240963, https://openalex.org/W3141567114, https://openalex.org/W3042739153, https://openalex.org/W6784472380, https://openalex.org/W2109877485, https://openalex.org/W3121022046, https://openalex.org/W2924689635, https://openalex.org/W2597441556, https://openalex.org/W3198395019, https://openalex.org/W2287926972, https://openalex.org/W6726972653, https://openalex.org/W2958285686, https://openalex.org/W3005509045, https://openalex.org/W3158459113, https://openalex.org/W4307861851, https://openalex.org/W3015471529, https://openalex.org/W2947832965, https://openalex.org/W3093410479, https://openalex.org/W2296509296, https://openalex.org/W3158507034, https://openalex.org/W3207891930, https://openalex.org/W4310398036, https://openalex.org/W3020687048, https://openalex.org/W3007481080, https://openalex.org/W4283212640, https://openalex.org/W2232882577, https://openalex.org/W4293713156, https://openalex.org/W4224940435, https://openalex.org/W2154776925, https://openalex.org/W3099185017 |
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