Research Publications for Abigail M Y Koay

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Publications ByKOAY, Abigail M Y

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  • Lu, Y., Koay, A., & Mayo, M. (2020). In silico comparison of continuous glucose monitor failure mode strategies for an artificial pancreas. In K. Bach, R. Bunescu, C. Marling, & N. Wiratunga (Eds.), Proc 5th International Workshop on Knowledge Discovery in Healthcare Data (KDH 2020) Vol. 2675 (pp. 53-57). Santiago de Compostela, Spain & Virtually: CEUR Workshop Proceedings. Retrieved from http://ceur-ws.org/Vol-2675/paper8.pdf

  • Compton, R., Frank, E., Patros, P., & Koay, A. (2020). Embedding Java classes with code2vec: improvements from variable obfuscation. In Proc IEEE/ACM 17th International Conference on Mining Software Repositories (MSR 2020) (pp. 243-253). New York, NY: ACM. doi:10.1145/3379597.3387445

  • 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

  • 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

  • Koay, A. (2019). Detecting high and low intensity Distributed Denial of Service (DDoS) attacks. (PhD Thesis, Victoria University of Wellington). Retrieved from http://hdl.handle.net/10063/8069

  • 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

  • Shafi, Q., Basit, A., Qaisar, S., Koay, A., & Welch, I. (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


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