Graduation Project Uses AI to Reduce Cybersecurity Analyst Alert Fatigue

University Media

The Networks and Cybersecurity Department held an open examination of a graduation project titled “Reducing SOC Alert Fatigue Using Random Forest and LightGBM,” which addresses the challenge of alert fatigue among Security Operations Center (SOC) analysts caused by the high volume of security alerts.

The project explored the use of the Random Forest and LightGBM machine-learning algorithms to improve the classification of security alerts and reduce false positives. The approach aims to enhance the efficiency of cyber threat detection, accelerate incident response, and improve the overall performance of Security Operations Centers.

The project was supervised by Dr. Sabri Al-Shaibani and evaluated by a scientific committee comprising Dr. Bashir Al-Tayyar and Eng. Mohammed Sultan. The committee commended the project for its scientific and practical relevance, as well as the efforts invested in applying artificial intelligence techniques to address a key challenge in cybersecurity operations.