Dr. Rana Mohammed Hassan, a faculty member in the Department of Computer Science, has published a research paper titled "Detection of Real-Time Distributed Denial of Service (DDoS) Attacks on Internet of Things (IoT) Networks Using Machine Learning Algorithms" in a reputable scientific journal published by Tech Science Press, indexed by both Clarivate and Scopus. The research was conducted in collaboration with researchers Zaid Sameer Mahdi and Naba Saeb Mahmoud from the Information Technology Center, and Nada Aziz Abdulhassan from the Continuing Education Center. The research focuses on designing a system to detect cyberattacks (DDoS) on IoT networks using machine learning algorithms. The proposed model is based on a two-phase process: identifying the essential features and building the detection model using the K-neighbors algorithm along with two classification algorithms. The performance evaluation of the model demonstrated its robust capabilities in detecting intrusions in IoT networks, showing improvements in accuracy, recall, and processing time.