The advancement of technology is changing digital communication systems rapidly. The development of network topologies is equally growing. Both trends have led to an increase in the number of cyber threats. Intelligent and adaptive Network Intrusion Detection Systems (NIDS) have become more necessary to counter the growing threat of cyber-attacks that include zero-day exploits, polymorphic malware, and other malicious traffic patterns. Sophisticated attacks could go undetected in large-scale network environments. This paper proposed the possibility of using structured machine learning in intrusion detection by utilizing the UNSW-NB15 Dataset. The analysis involved comparison of five machine learning algorithms: Random Forest, Naive Bayes, Neural Network, Gradient Boosting, and Stochastic Gradient Descent. The Random Forest model achieved high accuracy.