Cybersecurity-Intrusion-Detection-with-NSL-KDD

Cybersecurity Intrusion Detection with NSL-KDD: Comparative Analysis of DNN, XGBoost, andtheir Ensemble Models Explore a comprehensive analysis of intrusion detection in cybersecurity using the NSL-KDD dataset. This project applies various machine learning algorithms, including Deep Neural Networks (DNN), XGBoost, Navie Bayes, Logistic Regression (LR), and an Ensemble model (Ensemble XGBoost and DNN called XDNN). Among these models, the ensemble approach, combining the strengths of individual algorithms, yielded the most robust results in classifying network traffic into distinct attack types: Normal, DoS, Probe, R2L, and U2R. Dive into the findings and insights derived from this comparative analysis, shedding light on effective intrusion detection strategies for cybersecurity enhancement.