Big data mining is a process utilized to find hidden insights and patterns in large datasets. It can be used in various fields, such as healthcare, social sciences, and business. One of its applications in cybersecurity is analyzing network traffic to identify potential threats. The increasing volume of network traffic has led to the development of new techniques for analyzing and detecting cyber threats. These include the use of statistical techniques such as SVMs and Naive Bayes, as well as random forests. Traditional IDS systems are no longer able to identify complex attacks. This project aims to analyze the data collected from the NSL-KPDD dataset using different machine learning methods. Some of these include SVM with linear, RBF kernel, RVM with a polynomial, and Naive bayes. The performance of these methods is evaluated according to their accuracy, recall, F1-score, and precision. The results of a study revealed that SVM with the RBF kernel performed better than the other algorithms when it came to detecting network intrusions. It also outperformed Random Forests. The findings suggest that this algorithm could be useful in identifying network threats.