Enhancing Network Intrusion Detection Systems with Machine Learning: A Comparative Study with intrusion datasets

Network intrusion detection systems are crucial for preserving cybersecurity in the face of increasingly sophisticated cyberthreats that target international networks. Conventional approaches usually use signature-based methods, which are not very good at spotting new attacks like zero-day vulnerabilities. The current study explores machine learning-based methods to enhance NIDS's ability to detect known and unexpected threats. Our classifiers include Gaussian Naive Bayes, Bernoulli Naive Bayes, Logistic Regression, XGB Classifier, and K-Nearest Neighbors Classifier. For training and evaluation, we employ two popular datasets, UNSW-NB15 and NSL-KDD, which provide comprehensive representations of both malicious and benign traffic. Our results suggest that machine learning models may considerably boost intrusion detection's adaptability and accuracy, even though issues like feature selection, data imbalance, and processing efficiency need more research. The foundation of next-generation cybersecurity systems that can adjust to the constantly shifting threat landscape can be machine learning, as this study shows. NIDS can more precisely identify known and unknown attacks by using AIIML methods that learn from historical data to identify patterns and irregularities. This study examines a number of popular machine learning classifiers for intrusion detection using various datasets. The results shows that our study strengthens cybersecurity defense against different intrusion attacks.