The number of people and applications using the internet has increased substantially in recent years. The increased use of the internet has also resulted in various security issues. As the volume of data increases, cyber-attacks become increasingly sophisticated, exploiting vulnerabilities in network structures. The incorporation of modern technologies, particularly data mining, emerges as an essential method for analyzing huge amounts of data in real time, enabling the proactive detection of anomalies and potential security breaches. This research seeks to identify the most robust machine learning model for exploit detection. It applies five feature selection techniques and eight classification models to the UNSW-NB15 dataset. A comprehensive evaluation is conducted based on classification accuracy, computational efficiency, and execution time. The results demonstrate the efficiency of the Decision Tree model using Random Forest for feature selection in the real-time detection of exploit attacks, exhibiting an accuracy of 87.9%, along with a very short training (0.96 s) and testing time (0.29 ms/record).