Deep Ensemble Learning With Model Pruning for Efficient Ddos Attack Detection in IOT Networks

ABSTRACT The Deep Ensemble Learning with Model Pruning for DDoS Detection in IoT Networks is a security framework designed to detect distributed denial-of-service (DDoS) attacks in IoT environments. The system analyzes network traffic features such as packet rate, flow duration, and protocol type to identify malicious behavior. It combines multiple deep learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNN), using an ensemble approach to improve detection accuracy. Model pruning is applied to reduce computational complexity and make the system suitable for resource-constrained IoT devices. This approach enables efficient and reliable real-time detection of DDoS attacks in IoT networks. Keywords: Deep Ensemble Learning, DDoS Attack Detection, Internet of Things (IoT), Model Pruning, Network Security, Deep Learning, Intrusion Detection System.