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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Veröffentlicht in:Automatika Jg. 65; H. 2; S. 584 - 596
Hauptverfasser: Jose, Jisha, Judith, J. E.
Format: Journal Article Paper
Sprache:Englisch
Veröffentlicht: Ljubljana Taylor & Francis Ltd 02.04.2024
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Schlagworte:
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
ISSN:0005-1144, 1848-3380
Online-Zugang:Volltext
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Zusammenfassung: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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content type line 14
323049
ISSN:0005-1144
1848-3380
DOI:10.1080/00051144.2024.2304369