A Machine-Learning-Based Approach for the Detection and Mitigation of Distributed Denial-of-Service Attacks in Internet of Things Environments
The widespread adoption of Internet of Things (IoT) devices has significantly increased the exposure of cloud-based architectures to cybersecurity risks, particularly Distributed Denial-of-Service (DDoS) attacks. Traditional detection methods often fail to efficiently identify and mitigate these thr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/11/6012 |
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| Summary: | The widespread adoption of Internet of Things (IoT) devices has significantly increased the exposure of cloud-based architectures to cybersecurity risks, particularly Distributed Denial-of-Service (DDoS) attacks. Traditional detection methods often fail to efficiently identify and mitigate these threats in dynamic IoT/Cloud environments. This study proposes a machine-learning-based framework to enhance DDoS attack detection and mitigation, employing Random Forest, XGBoost, and Long Short-Term Memory (LSTM) models. Two well-established datasets, CIC-DDoS2019 and N-BaIoT, were used to train and evaluate the models, with feature selection techniques applied to optimize performance. A comparative analysis was conducted using key performance metrics, including accuracy, precision, recall, and F1-score. The results indicate that Random Forest outperforms other models, achieving a precision of 99.96% and an F1-score of 95.84%. Additionally, a web-based dashboard was developed to visualize detection outcomes, facilitating real-time monitoring. This research highlights the importance of efficient data preprocessing and feature selection for improving detection capabilities in IoT/Cloud infrastructures. Furthermore, the potential integration of metaheuristic optimization for hyperparameter tuning and feature selection is identified as a promising direction for future work. The findings contribute to the development of more resilient and adaptive cybersecurity solutions for IoT/Cloud-based environments. |
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| ISSN: | 2076-3417 |