PhD Degree Awarded to Mr. Abdulsalam Mohammed Hussein in Information Technology

Mr. Abdulsalam Mohammed Hussein Khaqo ‎was awarded a PhD degree in Information Technology for his dissertation titled: Enhancing Asynchronous Advantage Actor-Critic Algorithm for Cybersecurity ‎Intrusion Detection in Edge Computing‎, which was submitted to the Faculty of Computer and Information Technology – Sana’a University. The dissertation defense was held on Thursday‎, February 12, 2026.
The PhD Viva-Voce Committee, which was formed based on a resolution issued by the Graduate Studies and Scientific Research Council, consisted of the following:

# Committee Members Designation Position
1 Assoc. Prof. Naji Ali Abdullah Al-Shaibani ‎ Internal Examiner Chair
2 Prof. Sharaf Abdulhaq Mahyoub Al-Hamdi ‎ Main Supervisor Member
3 Assoc. Prof. Fouad Hassan Mohammed Yahya Abdulrazzaq ‎ External Examiner Member

The dissertation aimed to:
 Develop adaptive class balancing using Dynamic Synthetic Minority Oversampling ‎combined with Edited Nearest Neighbors (DSMOTEENN) on the X-IIoTID ‎dataset.‎
 Enhance the Worker component in the A3C algorithm to achieve faster convergence, ‎reduced gradient variance, stable updates, and improved parameter ‎synchronization.‎
 Design the hybrid HADT algorithm integrating the enhanced A3C (suitable for ‎continuous-feature attack detection) with Decision Tree (DT) classifiers (effective ‎for discrete-feature attacks).‎
The study yielded several key findings summarized as follows:
 The hybrid model effectively addressed data imbalance using DSMOTEENN, ‎improving rare-class detection and reducing error rates.‎
 The proposed enhancements to A3C within the EA3C model (including gradient ‎stabilization, weighted synchronization, and CNN/FFNN-based feature ‎extraction) achieved faster convergence, greater training stability, and superior ‎generalization compared to baseline models such as DT, DDQN, and AERL.‎
 The HADT algorithm achieved outstanding performance in Industrial IoT intrusion ‎detection, with average Accuracy, Precision, Recall, and F1-Score exceeding ‎‎99.8% in both binary and multi-class classification (up to 19 attack classes).‎
 ROC and AUC analyses showed near-perfect discrimination (0.9982–1.0000).‎
 The model demonstrated computational efficiency with reduced training time, ‎suitable inference latency, and optimized energy consumption—making it ‎appropriate for resource-constrained edge computing environments.‎
 The proposed hybrid methodology outperformed traditional and standalone ‎reinforcement learning approaches, establishing a new benchmark in Industrial IoT ‎cybersecurity analytics.‎
In light of these findings, the researcher recommended the following:‎
 Expanding evaluation across diverse industrial datasets (energy, manufacturing, ‎healthcare) to validate generalizability.‎
 Integrating graph-based deep learning models (e.g., Graph Neural Networks and ‎Graph Attention Networks) for detecting coordinated large-scale attacks.‎
 Exploring advanced reinforcement learning methods such as Proximal Policy ‎Optimization (PPO) and Soft Actor-Critic (SAC).‎
 Incorporating Meta-Reinforcement Learning to enhance rapid adaptation to emerging ‎attack patterns.‎
 Testing the system in real industrial environments with live traffic.‎
 Applying federated learning to enable secure model sharing across distributed edge ‎nodes.‎
 Enhancing interpretability using Explainable AI (XAI) techniques such as SHAP and ‎LIME.‎
 Applying model compression and quantization techniques for deployment on ultra-‎constrained embedded systems.‎
 Developing distributed Multi-Worker DRL architectures for cooperative defense in ‎large-scale IIoT networks.‎
 Moving toward Multi-Modal Intrusion Detection Systems integrating network flows, ‎device behavior, and sensor data.‎
The dissertation defense was attended by a number of academics, researchers, and specialists, students, colleagues, and the researcher’s family.