Strengthening Network Security with Advanced Defense Strategies for Robust Protection against DoS Attacks in Complex Environments

This repository presents a Reinforced Long Short-Term Memory (RLSTM)-based Intrusion Detection System (IDS) designed to accurately detect network intrusions across multiple benchmark cybersecurity datasets. The primary goal of the work is to develop a robust, generalizable deep learning framework capable of handling class imbalance, high-dimensional traffic features, and evolving attack patterns. The proposed RLSTM framework is evaluated on three widely recognized intrusion detection datasets: NSL-KDD CICIDS2017 CICIDS2019 These datasets represent different generations of network traffic and attack behaviors, enabling comprehensive cross-dataset validation.