In today’s fast-changing digital world, cyber-attacks have become more sophisticated and complex, making it challenging to accurately detect intrusions. These intrusions not only put data security at risk but also threaten the availability and integrity of important information, posing significant dangers to individuals, organizations, and society as a whole. This study proposes Machine Learning (ML) techniques for anomaly detection using the widely recognized CIC-IDS2017 dataset, which is a benchmark in cybersecurity research. The research evaluates each approach based on the performance of classifiers after preprocessing the data to remove irrelevant features that have minimal impact on attack detection. The results show that the Random Forest classifier, combined with the Correlation feature selection method, performs exceptionally well, achieving an impressive accuracy rate of 99.61%. These results show the degree of efficiency of a robust data preprocessing strategy in enhancing intrusion detection systems’ capabilities in classifying attack traffic, namely DDoS, Brute Force attacks, scans, Webbased attacks, Bots, Denial of Service, and Heartbleed vulnerabilities. The comparison between total accuracy provided by these techniques makes such research valuable in terms of insights for optimizing cybersecurity defenses against dynamically correlated threats thriving in this digital age.