Machine Learning Anomaly Detection Reduces False Positives More Effectively Than Rule-Based Systems
Security information and event management systems generate large volumes of alerts, many of which are false positives that consume analyst time and contribute to alert fatigue. Traditional rule-based detectors are easy to implement but struggle to balance high detection rates with low false positive rates in dynamic attack environments. According to a 2015 survey by the Ponemon Institute of over 600 U.S. IT and security practitioners, organizations spent on average nearly 21,000 hours per year and about $1.3 million in labor costs investigating security alerts that ultimately turned out to be false alarms. This paper investigates how Machine Learning-based anomaly detection can help reduce false positives compared to traditional rule-based threat detection methods. The study compared a rule-based detector with a machine learning detector on a Kaggle URL dataset with more than 800,000 labeled links. Both methods use only URL-based features, including length, special characters, and common phishing keywords. The rule-based approach is constructed from twelve human-designed heuristic rules frequently used in industry and the machine learning approach uses a Random Forest classifier trained on the same features. The rule-based system reaches about 55% accuracy and misses most phishing URLs. The Random Forest model reaches about 89% accuracy and can rank truly malicious cases above normal ones in about 95% of comparisons, while greatly reducing both false positives and false negatives. These results indicate that, even with simple URL-based features, machine learning anomaly detection can significantly reduce false positives compared with traditional rule-based systems and provide a more scalable foundation for operational cybersecurity.