Unveiling the IoT's dark corners: anomaly detection enhanced by ensemble modelling
The growing Internet of Things (IoT) landscape requires robust security; traditional rule-based systems are insufficient, driving the integration of machine learning (ML) for effective intrusion detection. This paper provides an inclusive overview of research efforts focused on harnessing ML methodo...
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| Vydané v: | Automatika Ročník 65; číslo 2; s. 584 - 596 |
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| Hlavní autori: | , |
| Médium: | Journal Article Paper |
| Jazyk: | English |
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Ljubljana
Taylor & Francis Ltd
02.04.2024
KoREMA - Hrvatsko društvo za komunikacije,računarstvo, elektroniku, mjerenja i automatiku Taylor & Francis Group |
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| ISSN: | 0005-1144, 1848-3380 |
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| Abstract | The growing Internet of Things (IoT) landscape requires robust security; traditional rule-based systems are insufficient, driving the integration of machine learning (ML) for effective intrusion detection. This paper provides an inclusive overview of research efforts focused on harnessing ML methodologies to fortify intrusion detection within IoT. Tailored feature extraction techniques are pivotal for achieving high detection accuracy while minimizing false positives. The study employs the IoT23 dataset from Kaggle and incorporates four optimization algorithms – Particle Swarm Optimizer, Whale-Pearson optimization algorithm, Harris-Hawks Optimizer, and Support Vector Machine with Particle Swarm optimization algorithm (SVM-PSO) – for feature extraction and selection. A comparison with ML algorithms such as logistic regression, decision tree and naïve Bayes classifier highlights Harris-Hawks Optimizer as the most effective. Furthermore, ensemble methods, particularly the fusion of random forest with HHO optimization, yield an impressive accuracy of 99.97%, surpassing AdaBoost and XGBoost approaches. This paper underscores the application of diverse ensemble learning techniques to enhance intrusion detection precision and efficiency within the intricate IoT landscape, effectively tackling the challenges posed by its complex and ever-changing nature. |
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| AbstractList | The growing Internet of Things (IoT) landscape requires robust security; traditional rule-based systems are insufficient, driving the integration of machine learning (ML) for effective intrusion detection. This paper provides an inclusive overview of research efforts focused on harnessing ML methodologies to fortify intrusion detection within IoT. Tailored feature extraction techniques are pivotal for achieving high detection accuracy while minimizing false positives. The study employs the IoT23 dataset from Kaggle and incorporates four optimization algorithms – Particle Swarm Optimizer, Whale-Pearson optimization algorithm, Harris-Hawks Optimizer, and Support Vector Machine with Particle Swarm optimization algorithm (SVM-PSO) – for feature extraction and selection. A comparison with ML algorithms such as logistic regression, decision tree and naïve Bayes classifier highlights Harris-Hawks Optimizer as the most effective. Furthermore, ensemble methods, particularly the fusion of random forest with HHO optimization, yield an impressive accuracy of 99.97%, surpassing AdaBoost and XGBoost approaches. This paper underscores the application of diverse ensemble learning techniques to enhance intrusion detection precision and efficiency within the intricate IoT landscape, effectively tackling the challenges posed by its complex and ever-changing nature. The growing Internet of Things (IoT) landscape requires robust security; traditional rule-based systems are insufficient, driving the integration of machine learning (ML) for effective intrusion detection. This paper provides an inclusive overview of research efforts focused on harnessing ML methodologies to fortify intrusion detection within IoT. Tailored feature extraction techniques are pivotal for achieving high detection accuracy while minimizing false positives. The study employs the IoT23 dataset from Kaggle and incorporates four optimization algorithms – Particle Swarm Optimizer, Whale-Pearson optimization algorithm, Harris-Hawks Optimizer, and Support Vector Machine with Particle Swarm optimization algorithm (SVM-PSO) – for feature extraction and selection. A comparison with ML algorithms such as logistic regression, decision tree and naïve Bayes classifier highlights Harris-Hawks Optimizer as the most effective. Furthermore, ensemble methods, particularly the fusion of random forest with HHO optimization, yield an impressive accuracy of 99.97%, surpassing AdaBoost and XGBoost approaches. This paper underscores the application of diverse ensemble learning techniques to enhance intrusion detection precision and efficiency within the intricate IoT landscape, effectively tackling the challenges posed by its complex and ever-changing nature. |
| Author | Judith, J. E. Jose, Jisha |
| Author_xml | – sequence: 1 givenname: Jisha surname: Jose fullname: Jose, Jisha organization: Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumarakovil, India – sequence: 2 givenname: J. E. surname: Judith fullname: Judith, J. E. organization: Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumarakovil, India |
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| Cites_doi | 10.1016/j.comcom.2021.05.024 10.1145/3409801 10.1109/JIOT.2020.3037733 10.3390/su14148707 10.1016/j.chaos.2021.111318 10.1109/TSE.2020.3030745 10.38094/jastt20165 10.1007/s11277-019-06986-8 10.1016/j.inpa.2016.08.002 10.1504/IJMHEUR.2018.091880 10.1109/ACCESS.2022.3178301 10.1016/j.aej.2022.02.063 10.1109/ICCMC48092.2020.ICCMC-00068 10.1109/CEC48606.2020.9185887 10.1016/j.applthermaleng.2020.115020 10.1016/j.jnca.2019.102526 10.1007/978-3-030-18732-3_8 10.1109/ITAIC.2019.8785556 10.1109/ISMSIT50672.2020.9254304 10.1109/CCWC.2019.8666588 10.1016/j.future.2019.02.028 10.1016/j.comcom.2022.01.022 10.1016/j.icte.2021.04.012 10.1016/j.swevo.2021.100868 10.1109/ICCSNT47585.2019.8962457 10.1109/ACCESS.2019.2933048 10.36548/jismac.2020.4.002 10.1007/s13369-020-05181-3 10.1002/ett.3803 |
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| SubjectTerms | Algorithms Anomalies Cybersecurity Decision trees Feature extraction Harris-Hawks optimizer Internet of Things Intrusion detection systems logistic regression Machine learning Naïve Bayes classifier Optimization algorithms Particle swarm optimization particle swarm optimizer Support vector machines System effectiveness Whale-Pearson optimization algorithm |
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| Title | Unveiling the IoT's dark corners: anomaly detection enhanced by ensemble modelling |
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