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Dr Abigail Koay

Research Fellow

Qualifications: BCompSci (UMP), PhD in Engineering (VUW)

Contact Details

Email: abigail.koay@waikato.ac.nz
Room: G.2.05
Phone: +6 07 838 4926
Website: https://www.waikato.ac.nz/staff-profiles/people/akoay

About Abigail

Dr. Abigail Koay is a Research Fellow at the Department of Computer Science, University of Waikato. She received her Bachelors of Computer Science from University Malaysia Pahang, Malaysia and her Ph.D. in Engineering (Network Security) from Victoria University of Wellington, New Zealand. Her Ph.D. research focuses on detecting Distributed Denial of Service (DDoS) attacks on large-scale networks. She studies the use of entropy-based features and the applicability of machine learning algorithms in detecting various intensity DDoS attacks. She also applies software-defined networking technology to address some of the network architecture issues in detecting DDOS attacks from multiple locations in the network. Abigail’s current research interest are Network Security, Traffic Anomaly Detection, Anomaly Detection in IoT Devices, Software-Defined Networking and Applied Machine Learning.

Research Interests

Anomaly Detection

Recent Publications

  • Podolskiy, V., Mayo, M., Koay, A., Gerndt, M., & Patros, P. (2019). Maintaining SLOs of cloud-native applications via self-adaptive resource sharing. In Proc 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019) (pp. 72-81). Umeå, Sweden: IEEE. doi:10.1109/SASO.2019.00018 Open Access version: https://hdl.handle.net/10289/12888

  • Koay, A., Welch, I., & Seah, W. K. G. (2019). Effectiveness of entropy-based features in high-and low-intensity DDoS attacks detection. In N. Attrapadung, & T. Yagi (Eds.), Proc 14th International Workshop on Security (IWSEC 2019), Advances in Information and Computer Security, LNCS 11689 (pp. 207-217). Tokyo, Japan: Springer. doi:10.1007/978-3-030-26834-3_12

  • Koay, A., Chen, A., Welch, I., & Seah, W. K. G. (2018). A new multi classifier system using entropy-based features in DDoS attack detection. In Proc 2018 International Conference on Information Networking (ICOIN) (pp. 162-167). Boston, USA: IEEE. doi:10.1109/icoin.2018.8343104

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