Graduation Project Develops Machine Learning Framework for Cyber Threat Detection

University Media

The Networks and Cybersecurity Department examined a graduation project developed by a group of female students in fulfillment of the requirements for a bachelor’s degree. Titled “A Machine Learning Framework for Cyber Threat Detection Using Static Malware Features and Network Traffic Analysis,” the project reflects the program’s focus on applying artificial intelligence technologies to strengthen cybersecurity and keep pace with rapid advances in this critical field.

The project aims to develop a machine learning-based framework for detecting cyber threats by integrating static malware feature analysis with network traffic analysis. This approach is designed to improve the accuracy of cyberattack detection, accelerate incident response, and enhance protection across diverse digital environments.

The project was supervised by Dr. Bilal Al-Samaei and evaluated by a scientific committee comprising Dr. Sabri Al-Shaibani and Dr. Bashir Al-Tayyar. The committee commended the project’s relevance and its contribution to addressing emerging developments in artificial intelligence and cybersecurity, while also recognizing the students’ efforts in applying machine learning techniques to the detection of cyber threats.