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A Comparative Study of Machine Learning Algorithms for IoT Cybersecurity

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Comparative evaluation of machine learning models for cybersecurity in IoT, suggesting performance improvements.

Random Forest and XGBoost models achieved accuracies exceeding 99.9%, demonstrating their effectiveness in IoT cybersecurity.
The analysis evaluates several machine learning algorithms including Logistic Regression, SVM, and ANN on the Bot-IoT dataset.
This comparative study highlights the importance of selecting appropriate algorithms for intrusion detection systems in IoT environments.
The findings support the need for advanced machine learning solutions in response to growing cyber threats against IoT networks.

Sachin et al. (2025) studied this question.

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View Full Paper

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Authors

Comparative evaluation of machine learning models for cybersecurity in IoT, suggesting performance improvements.

Random Forest and XGBoost models achieved accuracies exceeding 99.9%, demonstrating their effectiveness in IoT cybersecurity.
The analysis evaluates several machine learning algorithms including Logistic Regression, SVM, and ANN on the Bot-IoT dataset.
This comparative study highlights the importance of selecting appropriate algorithms for intrusion detection systems in IoT environments.
The findings support the need for advanced machine learning solutions in response to growing cyber threats against IoT networks.

Sachin et al. (2025) studied this question.

View Full Paper

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Bookmark