Particle Swarm Optimization (PSO) Based Feature Selection for DDOS Attack Detection in IOT

IoT networks in resource-constrained environments face significant challenges in detecting DDoS attacks due to the high computational demands required by traditional methods. To address this, the research focuses on optimizing feature selection through Particle Swarm Optimization (PSO) combined with deep learning techniques. By carefully selecting relevant features, the aim to reduce the computational load while maintaining or improving the accuracy of DDoS detection. This approach is particularly suited for IoT applications, where efficiency and quick response times are critical. This research aims to create a DDoS detection system that is used in resource-constrained IoT networks to guarantee strong security without sacrificing functionality.