Media of University of Babylon | Faculty Member from the Cybersecurity Department at the College of Information Technology, University of Babylon Achieves Distinguished Research Contributions in Network Security and Machine Learning

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Faculty Member from the Cybersecurity Department at the College of Information Technology, University of Babylon Achieves Distinguished Research Contributions in Network Security and Machine Learning

by : Duhaa Fadill Abbas

Date : 08/4/2026 34:44:06 PM

Veiw : 210

Faculty Member from the Cybersecurity Department at the College of Information Technology, University of Babylon Achieves Distinguished Research Contributions in Network Security and Machine Learning

Duhaa Fadill Abbas
In line with the College of Information Technology’s commitment at the University of Babylon to advancing scientific research and promoting the application of artificial intelligence in cybersecurity domains, Assistant Professor Dr. Mohammed Ibrahim Karim, a faculty member in the Cybersecurity Department, has achieved notable research contributions through the publication of several scholarly works in internationally peer-reviewed journals indexed in Scopus, in addition to participating in an international scientific conference. His research focuses on network security and machine learning applications.

Among his أبرز contributions is the study entitled:“AttnDEC-PPR: An Attention-driven Deep Embedded Clustering Model with Personalized PageRank Propagation for Robust Anomaly Detection in 5G Networks,” published in the International Journal of Intelligent Engineering and Systems (Q2). The study proposes a hybrid intelligent model for anomaly detection in 5G networks, leveraging deep learning and unsupervised clustering techniques. The proposed model achieved a high accuracy of 99.48%, demonstrating strong generalization capabilities across multiple datasets, thereby enhancing the security of 5G environments.

He also published a research paper titled:“Machine Learning-based Classification Models for Efficient DDoS Detection,” in the International Journal of Computing and Digital Systems (Q3). This study presents a comparative analysis of various machine learning models for detecting Distributed Denial of Service (DDoS) attacks. The findings reveal comparable performance across models, with Random Forest excelling in training speed, while Gradient Boosting achieved superior accuracy, contributing to informed model selection for strengthening cybersecurity defense systems.

As part of his international academic engagement, Dr. Karim presented a paper entitled:“Machine Learning-based Detection of Web Attacks Using Logistic Model Trees,” at the CSASE 2025 International Conference on Computer Science and Software Engineering, indexed by IEEE – Scopus. The research highlights the effectiveness of advanced machine learning techniques in detecting web-based attacks, achieving an accuracy of 99.54%, while identifying key influential features in intrusion detection, thereby supporting the development of more efficient web security systems.

These scholarly achievements reflect the advanced research activity within the Cybersecurity Department and underscore the College of Information Technology’s dedication to fostering high-quality academic research. They also contribute to the development of intelligent solutions for addressing cybersecurity challenges and enhancing the protection of modern networks and applications, in alignment with global technological advancements in this critical field.

Researches Link :
1- https://inass.org/wp-content/uploads/2025/08/2025113012.pdf

2- https://journal.uob.edu.bh/items/30cc71f5-e2df-4cd7-b21a-209ac8f01a6b

3- https://ieeexplore.ieee.org/3document/11053993