Software-Defined Networking (SDN) changes the way networks are managed by separating the control an‎d data planes, which makes them more flexible an‎d programmable. Its centralized control structure, on the other han‎d, makes it more vulnerable to assaults like Distributed Denial of Service (DDoS) that can overload the SDN controller. This study suggests a hybrid metaheuristic-based rule optimization system that uses both Genetic Algorithm (GA) an‎d Particle Swarm Optimization (PSO) to find an‎d stop DDoS attacks. A multi-objective fitness function that balances accuracy, resource efficiency, an‎d attack coverage is used by the framework to dynamically create the best flow rules. The suggested method combines detection an‎d rule development to reduce false positives an‎d improve scalability. The study adds a new combination of heuristic algorithms made specifically for SDN security, which makes for a strong an‎d flexible protection system. Using benchmark SDN datasets, we will test the modelʹs performance in real-world situations.

6/10/2025 12:00:00 AM

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1404/04/24

Metaheuristic-Based DDoS Attack Mitigation in Software-Defined Networks: A Hybrid Rule Optimization Approach

Software-Defined Networking , DDoS Mitigation , Metaheuristics , Genetic Algorithm , Particle Swarm Optimization