Deep Ensemble Based Efficient Framework For Network Attack Detection

In today's digital world, computer networks face a growing number of threats from cyber-attacks.Detecting these attacks quickly and accurately is essential to keeping systems safe.This project presents an improved method for detecting network attacks using a deep ensemble-based framework.Instead of relying on a single machine learning model, this system combines several deep learning models-such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and other advanced models-to work together as a team.This approach helps improve the overall accuracy and reduces the chances of missing an attack or raising a false alarm.To make the system more efficient, techniques like feature selection and lightweight model designs are used so that the system can run faster and use fewer resources.This makes it suitable for real-time monitoring of networks, where quick decisions are important.The framework is tested on wellknown datasets like NSL-KDD and CICIDS2017, and the results show that the ensemble method performs better than individual models in detecting various types of attacks.