Improving Cybersecurity: An In-depth Examination of Machine Learning Methods for Detecting Network Intrusions

Maintaining system integrity and protecting sensitive data is critical in today's digital age, underlining the need for strong network intrusion detection in cybersecurity. This paper offers a sophisticated Network Intrusion Detection System (NIDS) built with the NSL-KDD dataset. We developed and analyzed various machine learning models, including Support Vector Machine (SVM), XGBoost, K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), and Random Forest Classifier (RFC), to evaluate their accuracy, precision, and recall. Our study revealed significant variations in model efficacy following rigorous data pretreatment and hyperparameter tuning, emphasizing the necessity for tailored approaches to detecting intrusions. The findings provide useful insights for future research, which will aid in the creation of more resilient cybersecurity measures to meet growing threats.