0009-0004-0785-9069 About
Dr. Ahmad Houkan is a Syrian researcher who received his Ph.D. in Electrical Engineering from C. V. Raman Global University, Bhubaneswar, India. His academic specialization lies in Artificial Intelligence-driven cybersecurity, with a particular focus on Intrusion Detection Systems (IDS), Industrial Internet of Things (IIoT), and Industrial Control Systems (ICS) security.
He completed his Master of Technology (M.Tech) in Electrical Engineering at Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India, graduating with distinction and an overall score of 91.1%. He earned his Bachelor of Technology (B.Tech) in Electrical Drive Engineering from the University of Aleppo, Syria, where he ranked first in his class and received the Al Basil Award for Academic Excellence on three occasions.
His research interests include Artificial Intelligence, Machine Learning, Deep Learning, Explainable AI (XAI), Cybersecurity, IIoT Security, ICS Security, and Data-Driven Threat Detection. His work focuses on developing intelligent intrusion detection frameworks, explainable cybersecurity solutions, and robust security architectures for industrial environments.
Dr. Houkan has authored and co-authored numerous publications in SCI/SCIE and Scopus-indexed journals and international conferences. His recent research has focused on AI-powered intrusion detection systems, real-world cyberattack dataset generation using Mitsubishi PLC-based industrial testbeds, feature selection and optimization techniques, and advanced deep learning models for real-time cyberattack detection in industrial networks. He also serves as a reviewer for several reputable international journals and conferences in the fields of Artificial Intelligence, Cybersecurity, and Industrial Informatics.
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Publications (7)
- Spatially aware deep learning and explainable AI reveal geomorphic control hierarchy in a monsoon-dominated coastal lagoon Save
- Enhancing intrusion detection for network and IoT traffic with Dynamic Tanh: A comparative deep learning study Save
- Artificial intelligence approach to intrusion detection in industrial control systems with real world dataset generation and model evaluation Save
- Dynamic Tanh–enhanced transformer architecture for scalable and high-accuracy cyber threat detection in IoT environments Save
- Industrial IoT-Intrusion Detection Using PCA-Driven Decision Tree Save
- Enhancing Security in Industrial IoT Networks: Machine Learning Solutions for Feature Selection and Reduction Save
- Industry 5.0 and Sustainable Development in the Developing World Save