Federated Learning Enabled Fog Computing Framework for DDoS Mitigation in SDN Based IoT Networks

Abstract

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.