PROFESSOR
Prof. Zakaria Abou El Houda
zakaria.abouelhouda@inrs.ca
Zakaria Abou El Houda is an Assistant Professor at the Energy, Materials, and Telecommunications Center of the National Institute of Scientific Research (INRS-EMT), Canada. He is the founding director of the Resilient Cybersecurity Laboratory (RSEC) and a member of the INRS-UQO Joint Research Unit in Cybersecurity and Digital Trust.
Dr. Abou El Houda has contributed to significant research projects that apply machine learning techniques to intrusion detection systems (IDS), with a focus on enhancing their explainability and robustness. His research addresses critical challenges in the security of next-generation networks, including 5G and beyond, by developing resilient mechanisms to mitigate emerging threats.
Dr. Abou El Houda’s work has been published in leading scientific journals, and he is an active participant in international conferences and collaborative research initiatives that shape the future of secure, intelligent, and adaptive network systems. He was listed by Stanford University among the top 2% of the world’s most influential researchers in both 2024 and 2025.
Research Interests:
- Network Security
- Internet of Things (IoT)
- Secure and Resilient Next Generation Networks (B6G)
- Applied Artificial Intelligence for Cybersecurity
- Secure and Explainable Artificial Intelligence
- Network Orchestration and Automation (SDN/NFV)
- Secure and Frugal Edge Intelligence
- Blockchain and its applications
- Quantum Computing
NSERC Subject Codes
- 2508 Communications Networks
- 2700 Information Technology
- 2525 Wireless communication systems
- 2604 Intelligent systems applications
- 2800 Artificial Intelligence
PhD Students
Ines Guerziz
PhD Student
Research interests: Wireless Communication, Generative AI, Cybersecurity.
Ines Guerziz is currently pursuing a PhD at the RSEC Lab at the UMR INRS-UQO, Institut national de la recherche scientifique (INRS), Canada, focusing on the intersection of Generative AI, wireless communication, and cybersecurity. She earned her Master’s and Engineering degrees from the Engineering School of Computer Science (ESI-SBA) in Algeria, graduating with honors and achieving second rank in her promotion.
Abderrahman Elhajjout
PhD Student
Research interests: Machine Learning, Generative AI, Large Language Models.
Abderrahman Elhajjout is currently pursuing a PhD at the RSEC Lab at Université du Québec en Outaouais (UQO) in Outaouais, Canada, focusing on research in Large Language Models (LLMs) and the Internet of Things (IoT). He holds a Master’s degree in “Information Systems Engineering” from the Faculty of Sciences Semlalia in Marrakech, Morocco, with a specialization in information systems engineering.
Imene Bessaa
PhD student
Research interests: Federated Learning, IoT, Cybersecurity.
Imene Bessaa is currently pursuing a PhD at the RSEC Lab, UMR INRS-UQO, Institut national de la recherche scientifique (INRS), Canada. Her research focuses on the security and reliability of distributed models and artificial intelligence techniques to detect cyber threats, enhance system resilience, and protect user data within intelligent transportation infrastructures.
Hadi Youssef
PhD Student
Research interests: Federated Learning, IoT, Cybersecurity.
Hadi Youssef is currently pursuing a PhD at the RSEC Lab (UMR INRS-UQO) at the National Institute of Scientific Research (INRS), Canada. His research focuses on leveraging distributed models and advanced artificial intelligence methods to identify cyber threats and to enhance the security, resilience, and trustworthiness of Internet of Things (IoT) environments.
Kaouthar Merzouki
PhD Student
Research interests: Graph Neural Networks, Cybersecurity, IoT, APT Attacks, Artificial Intelligence.
Kaouthar Merzouki is a PhD student at the RSEC Lab, UMR INRS-UQO, Institut national de la recherche scientifique (INRS), Canada. Her research focuses on applying Graph Neural Networks (GNNs) to cybersecurity, with a particular interest in intrusion detection and APT attacks in IoT environments. She holds a Master’s degree in Big Data and Internet of Things (IoT) from the Institut National des Postes et Télécommunications (INPT), Morocco.
Elhabib Sbihi
PhD Student
Research interests: Large Language Models, ORAN, 6G Networks.
Elhabib SBIHI is a PhD researcher at the RSEC Lab (UMR INRS-UQO) at INRS, Canada. His work focuses on applying Large Language Models (LLMs) to enhance cybersecurity, anomaly detection, and automated orchestration within Open RAN (O-RAN) and modern telecommunication systems.
Ali Mehrban
PhD student ( Incoming)
Research interests: Machine Learning, O-RAN, 6G Networks
Ali Mehrban has been working as Telecom Network Engineer with over a decade of industry experience in network design, optimization, and AI-driven cybersecurity solutions. He holds a Master’s degree in Communications and Signal Processing from Newcastle University and has a deep research focus on AI applications in cybersecurity, 6G networks, O-RAN, edge computing, and intelligent IoT communication. His work bridges practical industry applications with academic research, with multiple publications in areas such as network traffic analysis, machine learning for wireless communication, and blockchain-based cybersecurity. Ali is currently pursuing a PhD, aiming to contribute innovative AI-powered solutions to the field of telecommunications and Cybersecurity. He is proficient in Python, MATLAB, and advanced network technologies.
PostDoc
Adam Kadi
PostDoc
Research interests: Machine learning, Quantum Computing, Intent-Based Networking.
Adam Kadi earned a bachelor’s degree in computer science, followed by a master’s degree in computer science with a specialization in Information Systems Security from the University of Caen Normandy. He earned his Ph.D. in Computer Science, where he developed artificial intelligence-based approaches for high-rate DDoS attack mitigation. He is currently a postdoctoral fellow at the INRS–UQO Joint Research Unit on Cybersecurity and Digital Trust. His research focuses on artificial intelligence, machine learning, and network security, as well as on advanced approaches to quantum computing and quantum machine learning. He is interested in the application of hybrid quantum-classical models for anomaly detection and network infrastructure security.
Interns
Maroua Cherifi
Intern
Research interests: Machine learning, Federated Learning, Healthcare.
Maroua Cherifi is a data science graduate from the University of Quebec at Trois-Rivières (UQTR), with a background in networks and telecommunications from the University of Science and Technology Houari Boumediene in Algeria. She has been working on multiple projects in machine learning, including work at the Institut National de la Recherche Scientifique (INRS) in Canada and UQTR. Her recent work focuses on cutting-edge AI applications, such as object detection using the YOLOv8 model, and Federated Learning approaches applied to medical data.
Alumnis
- PhD
- H. Amari, Smart models for security enhancement in the internet of vehicles (co-supervised with Prof. Khoukhi), 2023.
- Master
- M. Bernoussi, Blockchain integration with the Internet of things, 2023.
- A. Idelhaj, Leveraging Large Language Models to enhance Network Attack Detection in IoT environments, 2024.
- M. A. Fall, Reinforcement Learning for intrusion detection system, 2025.
- H. Youssef, Efficient Federated Learning Under Heterogeneous Distributions, 2025.
- H. Bouslama, LLM Architecture for Smart Contract Vulnerability Detection, 2025.
- Research Associates
- A. Selamnia, Securing Industrial Internet of Things (IIoT) networks using Quantum Machine Learning, 2025.
- A. Kadi, Quantum Machine Learning for mitigating Distributed Denial-of-Service (DDoS) attacks, 2025.