Dr Segun Popoola

Segun’s research focuses on the intersection of cyber security, artificial intelligence (AI), and smart critical infrastructure. His work has significantly contributed to advancing cyber security through innovative research and teaching. Notably, his projects, such as those funded by Innovate UK, reflect his expertise in federated learning, IoT security, and AI-driven solutions.

Recognised among the world's top 2% most-cited scientists and endorsed by The Royal Society for his exceptional talent, Segun's contributions to the field are both impactful and inspirational.

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Background

Segun was previously a Lecturer in Cyber Security and Artificial Intelligence at Manchester Metropolitan University, UK, and has held various academic positions in Nigeria.

His research interests include cyber security with a focus on intrusion detection, AI (specifically deep learning and federated learning), Internet of Things (IoT), smart critical infrastructure, and wireless communication, and he is a member of the Cyber Security and Networking Research Group.

He has led and contributed to several innovative projects, notably in partnership with Innovate UK, and has been actively involved in cutting-edge research contributing to over 100 publications with more than 2400 citations, h-index of 29 on Google Scholar.

He has been recognized as one of the world's top 2% most-cited scientists and endorsed for his global exceptional talent in security and privacy by The Royal Society, UK.

Spoken Languages

• English
• Yoruba

Research interests

• Cybersecurity of smart critical infrastructure
• Artificial Intelligence (deep learning and federated learning)
• Security of AI systems
• Internet of Things
• Wireless communications

Areas of research supervision

• Cybersecurity of smart critical infrastructure
• Artificial Intelligence (deep learning and federated learning)
• Security of AI systems
• Internet of Things
• Wireless communications

Teaching

• Digital & Network Security Forensics
• Penetration Testing

Qualifications

• Doctor of Philosophy (PhD) in Cyber Security and Artificial Intelligence, Manchester Metropolitan University, UK.
• Master of Engineering (MEng) in Information and Communication Engineering (Distinction), Covenant University, Nigeria.
• Bachelor of Technology (BTech) in Electronic and Electrical Engineering (First Class), Ladoke Akintola University of Technology, Nigeria.

Memberships, editorial boards

• Member of Institute of Electrical & Electronic Engineers (MIEEE), No. 96166851.
• Registered Engineer, The Council for the Regulation of Engineering in Nigeria (COREN), R.66381.

Research grants, consultancy, knowledge exchange

• Academic Supervisor, Innovate UK’s Knowledge Transfer Partnership (KTP) project in partnership with I Want Plants Limited - development of novel data science capabilities to understand the benefits arising from living green infrastructure, £253K, 2024-2026.
• Academic Supervisor, Innovate UK’s KTP project in partnership with Oaktree Power Ltd - development of novel artificial intelligence capabilities to improve flexible energy management for the corporate real estate sector, £219K, 2023-2025.
• Project Lead, Innovate UK’s Accelerated Knowledge Transfer to Innovate (AKT2I) project in partnership with Acquacheck Engineering Ltd - assessment of web security vulnerabilities for Aquacheck’s IoT platform and identification of the best strategies to mitigate the risks under different deployment and scalability scenarios, £26K, 2022-2023.
• Federated Learning Specialist, European Research Executive Agency Horizon Europe Marie Sklodowska-Curie Actions Staff Exchanges project - Rural Environmental Monitoring via ultra wide-ARea networKs And distriButed federated Learning (REMARKABLE), 2023-2026.

Selected recent publications

Imoize, A. L., Montlouis, W., Obaidat, M. S., Popoola, S. I., & Hammoudeh, M. (Eds.). (2024). Computational Modeling and Simulation of Advanced Wireless Communication Systems. CRC Press.

Popoola, S. I., Imoize, A. L., Hammoudeh, M., Adebisi, B., Jogunola, O., & Aibinu, A. M. (2023). Federated Deep Learning for Intrusion Detection in Consumer-Centric Internet of Things. IEEE Transactions on Consumer Electronics. doi: 10.1109/TCE.2023.3347170.

Popoola, S. I., Ande, R., Adebisi, B., Gui, G., Hammoudeh, M., & Jogunola, O. (2022). Federated deep learning for zero-day botnet attack detection in IoT-edge devices. IEEE Internet of Things Journal, 9(5), 3930-3944.

Popoola, S. I., Adebisi, B., Hammoudeh, M., Gui, G., & Gacanin, H. (2021). Hybrid deep learning for botnet attack detection in the internet-of-things networks. IEEE Internet of Things Journal, 8(6), 4944-4956.

Popoola, S. I., Adebisi, B., Ande, R., Hammoudeh, M., Anoh, K., & Atayero, A. A. (2021). smote-drnn: A deep learning algorithm for botnet detection in the internet-of-things networks. Sensors, 21(9), 2985.

Popoola, S. I., Adebisi, B., Hammoudeh, M., Gacanin, H., & Gui, G. (2021). Stacked recurrent neural network for botnet detection in smart homes. Computers & Electrical Engineering, 92, 107039.

Popoola, S. I., Adebisi, B., Ande, R., Hammoudeh, M., & Atayero, A. A. (2021). Memory-efficient deep learning for botnet attack detection in IoT networks. Electronics, 10(9), 1104.

Popoola, S. I., Ande, R., Fatai, K. B., & Adebisi, B. (2021). Deep bidirectional gated recurrent unit for botnet detection in smart homes. Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics: Theories and Applications, 29-55.

Recent presentations and conferences

Invited Speaker, Digitalization in science: breaking new frontiers with artificial intelligence at the Avant-Garde Conference organized by American Chemical Society, Ladoke Akintola University of Technology Chapter, Nigeria, October 2022.

Invited Speaker and Trainer, Python for scientific computing, artificial intelligence, and cyber security bootcamp organized by the Saudi Aramco Cyber Security Chair at the Interdisciplinary Research Center for Intelligent Secure Systems, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia, December 2023

Popoola, S. I., Gui, G., Adebisi, B., Hammoudeh, M., & Gacanin, H. (2021, September). Federated deep learning for collaborative intrusion detection in heterogeneous networks. In 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) (pp. 1-6). IEEE.