Dr Abigail Koay
Research Fellow in Cyber Security
School of Electrical Engineering and Computer Science
+61 7 336 52787
| Featured projects | Duration |
|---|---|
|
Real-time Automatic Detection of Cyber-Attacks using Weakly Supervised Learning UQ Cyber Seed Funding |
2021–2022 |
Journal Articles
Koay, Abigail M. Y., Ko, Ryan K. L, Hettema, Hinne and Radke, Kenneth (2022). Machine learning in industrial control system (ICS) security: current landscape, opportunities and challenges. Journal of Intelligent Information Systems, 60 (2), 1-29. doi: 10.1007/s10844-022-00753-1
McCarthy, Harry, Koay, Abigail, Dawson, Michael, Kent, Kenneth B. and Patros, Panos (2021). Benchmarking and learning garbage collection delays for resource‐restricted graphical user interfaces. Software: Practice and Experience, 52 (1) spe.3005, 172-193. doi: 10.1002/spe.3005
Shafi, Qaisar, Basit, Abdul, Qaisar, Saad, Koay, Abigail and Welch, Ian (2018). Fog-assisted SDN controlled framework for enduring anomaly detection in an IoT network. IEEE Access, 6, 73713-73723. doi: 10.1109/access.2018.2884293
Conference Papers
Gunasinghe, Hansi, McKelvie, James, Koay, Abigail and Mayo, Michael (2022). Domain Generalisation for Glaucoma Detection in Retinal Images from Unseen Fundus Cameras. 14th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2022, Ho Chi Minh City, Vietnam, 28–30 November 2022. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-21967-2_34
Wilton, Jonathan, Koay, Abigail M. Y., Ko, Ryan K. L., Miao Xu and Ye, Nan (2022). Positive-unlabeled learning using random forests via recursive greedy risk minimization. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, United States, 29 November - 1 December 2022. New Orleans, LA, United States: Neural information processing systems foundation.
Koay, Abigail M. Y., Xie, Miao, Ko, Ryan K. L., Sterner, Charles, Choi, Taejun and Dong, Naipeng (2022). SDGen: a scalable, reproducible and flexible approach to generate real world cyber security datasets. 1st International Conference on Ubiquitous Security (UbiSec), Guangzhou, Peoples Republic of China, 28-31 December 2021. Singapore, Singapore: Springer Singapore. doi: 10.1007/978-981-19-0468-4_8
Choi, Taejun, Ko, Ryan K. L., Saha, Tapan, Scarsbrook, Joshua, Koay, Abigail M. Y., Wang, Shunyao, Zhang, Wenlu and St Clair, Connor (2021). Plan2Defend: AI planning for cybersecurity in smart grids. 2021 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia), Brisbane, QLD, Australia, 5-8 December 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/isgtasia49270.2021.9715679
Gunasinghe, Hansi, McKelvie, James, Koay, Abigail and Mayo, Michael (2021). Comparison of pretrained feature extractors for glaucoma detection. International Symposium on Biomedical Imaging (ISBI), Nice, France, 13-16 April 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/isbi48211.2021.9434082
Compton, Rhys, Frank, Eibe, Patros, Panos and Koay, Abigail (2020). Embedding Java classes with code2vec: improvements from variable obfuscation. International Conference on Mining Software Repositories, Seoul, Republic of Korea, June 2020. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3379597.3387445
Lu, Yunjie, Koay, Abigail and Mayo, Michael (2020). In silico comparison of continuous glucose monitor failure mode strategies for an artificial pancreas. CEUR Workshop , Santiago de Compostela, Spain & Virtually, 29-30 August 2020. Aachen, Germany: Rheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V.
Podolskiy, Vladimir, Mayo, Michael, Koay, Abigail, Gerndt, Michael and Patros, Panos (2019). Maintaining SLOs of cloud-native applications via self-adaptive resource sharing. 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), Umea, Sweden, 16-20 June 2019. New York, NY, United States: IEEE. doi: 10.1109/saso.2019.00018
Koay, Abigail, Welch, Ian and Seah, Winston K. G. (2019). (Short Paper) effectiveness of entropy-based features in high- and low-intensity DDoS attacks detection. International Workshop on Security, Tokyo, Japan, 28-30 August 2019. Cham, Switzerland: Springer . doi: 10.1007/978-3-030-26834-3_12
Koay, Abigail, Chen, Aaron, Welch, Ian and Seah, Winston K. G. (2018). A new multi classifier system using entropy-based features in DDoS attack detection. 32nd International Conference on Information Networking (ICOIN), Chiang Mai, Thailand, 10-12 January 2018. New York, NY, United States: IEEE. doi: 10.1109/ICOIN.2018.8343104