DDoS attacks require efficient detection due to challenges like latency, false positives, and resource inefficiency, especially in IoT and Fog-SDN setups. A framework combining ML and DL for real-time DDoS detection was evaluated against Logistic Regression, Random Forest, and CNN using benchmark datasets. Key metrics included accuracy, precision, recall, F1-score, false positive rate, latency, and resource use. The framework achieved 98.3% accuracy, surpassing CNN (95.6%), Random Forest (91.5%), and Logistic Regression (86.8%). Precision, recall, and F1-score were 98.7%, 97.8%, and 98.2%. False positive rates were 2.1%, compared to CNN (4.3%), Random Forest (6.4%), and Logistic Regression (8.2%). Latency was 30–110 ms for 100–500 requests in Fog-SDN versus 50–180 ms in cloud setups. Resource utilization was efficient: fog nodes 70%, cloud 60%, and IoT devices 40%. The proposed framework ensures high accuracy, low latency, and efficient resource use, perfect for real-time DDoS detection in Fog-SDN environments.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Kumar J and Arul Leena Rose P J;
Methodology: Kumar J;
Visualization: Kumar J;
Investigation: Kumar J and Arul Leena Rose P J;
Supervision: Arul Leena Rose P J;
Validation: Kumar J;
Writing- Reviewing and Editing: Kumar J and Arul Leena Rose P J; All authors reviewed the results and approved the final version of the manuscript.
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Arul Leena Rose P J
Department of Computer Science, Faculty of Science and Humanities, SRMIST, Kattankulathur, Chennai, Tamil Nadu, India.
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Cite this article
Kumar J and Arul Leena Rose P J, “Federated Learning Enabled Fog Computing Framework for DDoS Mitigation in SDN Based IoT Networks”, Journal of Machine and Computing, vol.5, no.3, pp. 1492-1502, July 2025, doi: 10.53759/7669/jmc202505118.
