Comprehensive Review: Network Intrusion Detection System Using Machine Learning on NSLKDD Dataset

This review paper analyzes a comprehensive Network Intrusion Detection System (NIDS) implementation utilizing machine learning and deep learning classifiers on the NSL-KDD dataset.The project demonstrates a multi-faceted approach involving rigorous data preprocessing, feature engineering via Pearson correlation analysis, and comparative evaluation of eight distinct machine learning models.Results show K-Nearest Neighbors achieving peak performance at 98.55% binary classification accuracy, while deep learning models (LSTM, MLP) demonstrated competitive accuracy rates above 96%.This paper examines the project's methodology, implementation, performance metrics, and implications for cybersecurity applications.The research contributes to the growing body of evidence supporting machine learning's efficacy in automated intrusion detection while identifying challenges and future research directions.