Dr Badeea Al Sukhni

Badeea began her academic journey with a Bachelor degree in Telecommunication and Software Engineering in Jordan, graduating with distinction. She worked as a Quality Assurance and R&D Engineer, contributing to embedded systems projects such as electrical unmanned ground vehicles and ventilator systems. These industry roles deepened her interest in system resilience, cybersecurity, and digital forensics investigation.

She pursued postgraduate studies in the UK, completing an MSc with Distinction in Cybersecurity and Digital Forensics at the University of Westminster, followed by a fully funded PhD at Canterbury Christ Church University. Her doctoral research focused on detecting multilayer attacks in IoT networks using machine learning, with emphasis on feature selection, intrusion detection, and human–machine teaming. Her thesis was awarded with no corrections, and she won First Place in the university’s 2024 Three Minute Thesis (3MT) competition.

As an academic, Badeea has taught for three years computing and engineering students at Canterbury Christ Church University (UK) and currently works as an IT Lecturer in the School of Information Technology at Whitecliffe College. She teaches cybersecurity and web development courses, and supervises MSc projects in cybersecurity and machine learning. She also contributes to academic events, including the 2025 Tech Exhibition.

Her research has been published in peer-reviewed journals such as IEEE and MDPI Sensors, and presented at international conferences including IEEE WF-IoT (Portugal), Springer CNC 2022 (India), and the UK Government Security and Policing Exhibition. Her current research continues to explore AI applications in cybersecurity, healthcare, and sustainability.
Dr Badeea is an Associate Fellow of the Higher Education Academy (AFHEA) and a member of IEEE Women in Engineering. She actively supports student mentorship and the empowerment of women in STEM.

Additional Information
Google Scholar: https://scholar.google.com/citations?user=iyEikx0AAAAJ&hl=en
Journal Articles and Conference Papers

1. Al Sukhni, B. A., Manna, S. K., Dave, J. M., & Zhang, L. Evaluation of the Semi-Automated Intrusion Detection System (SAIDS) Against Multilayer IoT Attacks in Simulated and Real-World Environments. Submitted to IEEE Security & Privacy.
2. Sukhni, B. A., Manna, S. K., Dave, J. M., & Zhang, L. (2024). Extracting Optimal Number of Features for Machine Learning Models in Multilayer IoT Attacks. Sensors, 24(24), 8121. https://doi.org/10.3390/s24248121
3. Al Sukhni, B. A., Manna, S. K., Dave, J. M., & Zhang, L. (2023). Exploring Optimal Set of Features in Machine Learning for Improving IoT Multilayer Security. 2023 IEEE 9th World Forum on Internet of Things (WF-IoT), Aveiro, Portugal, pp. 1–6.
4. Al Sukhni, B., Dave, J. M., Manna, S. K., & Zhang, L. (2022, December). Investigating the Security Issues of Multi-layer IoT Attacks Using Machine Learning Techniques. In 2022 Human-Centered Cognitive Systems (HCCS), pp. 1–9. IEEE.
5. Al Sukhni, B. A., Manna, S. K., Dave, J. M., & Zhang, L. (2023). Machine Learning-Based Solutions for Securing IoT Systems Against Multilayer Attacks. In Communications in Computer and Information Science. Cham: Springer Nature Switzerland, pp. 140–153.
6. Al Sukhni, B. A., Kumar Mohanta, B., Kumar Dehury, M., & Kumar Tripathy, A. (2023). A Novel Approach for Detecting and Preventing Security Attacks Using Machine Learning in IoT. 14th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India, pp. 1–6.
7. Al Sukhni, B., Mohanta, B. K., Alhussain, M., Aslam, S., & Aurangzeb, K. (2023). Unsolicited Traffic Investigation on IoT Devices Using Machine Learning. Transactions on Emerging Telecommunications Technologies, Wiley. (Under publication).
8. Mohanta, B. K., Dehury, M. K., Sukhni, B. A., & Mohapatra, N. (2022). Cyber Physical System: Security Challenges in Internet of Things Systems. In 6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), Dharan, Nepal, pp. 117–122.

Poster Presentations

1. Al Sukhni, B. A., Manna, S. K., Dave, J. M., & Zhang, L. (2024). Investigating Security Issues (Multilayer Attacks) on IoT Devices Using Machine Learning. Presented at the Early Career Researchers Session, UK Government Security and Policing 2024 Exhibition, Farnborough International Exhibition and Conference Centre, March 2024.
   Available at: https://researchspace.canterbury.ac.uk/975y7/safeguarding-iomt-semi-automated
2. Al Sukhni, B., Manna, S., Dave, J., & Zhang, L. (2022). Investigating the Security Issues of Multi-layer IoMT Attacks Using Machine Learning Techniques.
   Presented at: Exploring Research and Development in the MedTech, Life Science and Healthcare Sectors, Maidstone Innovation Centre. Available at: https://repository.canterbury.ac.uk/item/9315y/investigating-the-security-issues-of-multi-layer-iomt-attacks-using-machine-learning-techniques