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Enhancing IoT Security: A Comparative Analysis of Machine Learning and Deep Learning Techniques for Botnet Detection

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This analysis compares ML and DL techniques for botnet detection in IoT, suggesting improved performance through data augmentation.

Data augmentation significantly enhances model performance in detecting IoT threats, improving precision and recall.
Both decision tree and k-nearest neighbors achieved top metrics with an accuracy of 0.98 in detecting Mirai botnet attacks.
The study utilized the cic iot dataset 2023 to evaluate various ML and DL models for IoT security applications.
Findings indicate that balanced datasets are crucial, as SGD's performance suffered due to class imbalance.

Almousa et al. (2025) studied this question.

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View Full Paper

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Authors

This analysis compares ML and DL techniques for botnet detection in IoT, suggesting improved performance through data augmentation.

Data augmentation significantly enhances model performance in detecting IoT threats, improving precision and recall.
Both decision tree and k-nearest neighbors achieved top metrics with an accuracy of 0.98 in detecting Mirai botnet attacks.
The study utilized the cic iot dataset 2023 to evaluate various ML and DL models for IoT security applications.
Findings indicate that balanced datasets are crucial, as SGD's performance suffered due to class imbalance.

Almousa et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark