Media of University of Babylon | Researchers Publish a Scientific Study on Intelligent Detection of DDoS Attacks in IoT Environments

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Researchers Publish a Scientific Study on Intelligent Detection of DDoS Attacks in IoT Environments

by : Duhaa Fadill Abbas

Date : 13/5/2025 45:57:07 PM

Veiw : 98

Researchers Publish a Scientific Study on Intelligent Detection of DDoS Attacks in IoT Environments

Duhaa Fadill Abbas
As part of the College of Information Technology’s ongoing commitment to advancing scientific research and addressing contemporary technological challenges, graduate student Iman Karkawi, pursuing her Master’s degree in the Department of Information Networks, in collaboration with Prof. Dr. Mahdi Abadi Mani, Head of the Department of Intelligent Medical Systems at the College of Science, Al-Mustaqbal University, has published a distinguished research study titled:

"A Fog Computing-Based Machine Learning Framework for DDoS Attacks Detection: Balancing Offline and Real-Time Analysis for IoT Data"

The paper was published in the International Journal of Safety and Security Engineering, a Scopus-indexed journal ranked in Q2, which specializes in topics related to engineering safety and security.

This study addresses one of the most critical security threats in the field of Internet of Things (IoT)—the growing risk of Distributed Denial of Service (DDoS) attacks, which pose a serious threat to the reliability, stability, and availability of digital infrastructure. The authors propose an innovative and intelligent framework that integrates fog computing, machine learning algorithms, and lightweight encryption techniques to enable precise and efficient detection of DDoS attacks. The framework is designed to balance offline training with real-time detection, thus enhancing the responsiveness and scalability of IoT defense mechanisms.

The proposed solution demonstrates a high capacity to strengthen IoT network security by relying on real-world datasets for offline model training, which is subsequently deployed within the fog layer to ensure secure communication and resource-efficient detection during operation. Experimental results confirm that the framework achieves near-perfect accuracy in identifying various forms of DDoS attacks, significantly outperforming conventional methods in terms of precision and efficiency.

This research achievement reflects the College of Information Technology’s steadfast dedication to promoting high-impact scientific contributions, supporting its students and faculty in developing innovative solutions to global technological issues, and reinforcing the university’s standing in the international research community.

Access the published paper :
https://www.iieta.org/journals/ijsse/paper/10.18280/ijsse.140620