A Hybrid Approach for Detecting DDoS Attacks in Software-Defined Networks

In the realm of Software-Defined Networks (SDNs), which revolutionize network management, a new approach aims to detect and prevent distributed DDoS attacks. By combining statistical analysis and machine learning, this method achieves impressive results, with a 100% detection rate and 99.80% accuracy in recognizing DDoS assaults. Key metrics like Flow Count, Speed of Flow Entries, and Ratio of Pair-Flow Entries are used to identify anomalies in network traffic. The study emphasizes the importance of a multi-pronged strategy to safeguard networks against evolving cyber threats using Software-Defined networking.