Dr Gia Bao Doan
Postdoctoral Researcher
School of Computer and Mathematical Sciences
Faculty of Sciences, Engineering and Technology
My research is about Trustworthy Machine Learning System, security aspect of Machine Learning and Deep Learning.
My research is about trustworthy and security of Machine Learning and Deep Learning systems.
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Appointments
Date Position Institution name 2022 - ongoing Postdoctoral Research Associate The University of Adelaide 2014 - 2018 Process and Equipment Engineer Intel Products Vietnam -
Language Competencies
Language Competency English Can read, write, speak, understand spoken and peer review Vietnamese Can read, write, speak, understand spoken and peer review -
Education
Date Institution name Country Title 2018 - 2022 University of Adelaide Australia Doctor of Philosophy 2012 - 2014 RMIT University Vietnam Vietnam Master of Engineerings 2007 - 2012 Danang University of Technology Vietnam Bachelor of Electronics and Communication Engineering -
Research Interests
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Journals
Year Citation 2023 Doan, B. G., Yang, S., Montague, P., De Vel, O., Abraham, T., Camtepe, S., . . . Ranasinghe, D. C. (2023). Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness. Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, 37, 14783-14791.
Scopus22022 Doan, B. G., Xue, M., Ma, S., Abbasnejad, E., & Ranasinghe, D. C. (2022). TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems. IEEE Transactions on Information Forensics and Security, 17, 3816-3830.
Scopus20 WoS92021 Gao, Y., Kim, Y., Doan, B. G., Zhang, Z., Zhang, G., Nepal, S., . . . Kim, H. (2021). Design and Evaluation of a Multi-Domain TrojanDetection Method on Deep Neural Networks. IEEE Transactions on Dependable and Secure Computing, 19(4), 1-14.
Scopus33 WoS6 Europe PMC2 -
Conference Papers
Year Citation 2024 Doan, B. G., Nguyen, D. Q., Montague, P., Abraham, T., De Vel, O., Camtepe, S., . . . Ranasinghe, D. C. (2024). Bayesian Learned Models Can Detect Adversarial Malware for Free. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14982 LNCS (pp. 45-65). Springer Nature Switzerland.
DOI2023 Doan, B. G., Yang, S., Montague, P., De Vel, O., Abraham, T., Camtepe, S., . . . Ranasinghe, D. C. (2023). Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness. In B. Williams, Y. Chen, & J. Neville (Eds.), THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 12 (pp. 14783-14791). Online: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2022 Yang, S., Doan, B. G., Montague, P., De Vel, O., Abraham, T., Camtepe, S., . . . Kanhere, S. S. (2022). Transferable Graph Backdoor Attack. In ACM International Conference Proceeding Series (pp. 321-332). Online: ACM.
DOI Scopus13 WoS42022 Doan, B. G., Abbasnejad, E., Shi, J. Q., & Ranashinghe, D. C. (2022). Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense. In INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162 Vol. 162 (pp. 15 pages). Baltimore, MD: JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020 Doan, B. G., Abbasnejad, M., & Ranasinghe, D. C. (2020). Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems. In Annual Computer Security Applications Conference, ACSAC 2020 (pp. 897-912). online: ACM.
DOI Scopus154 WoS58 -
Preprint
Year Citation 2024 Doan, B. G., Shamsi, A., Guo, X. -Y., Mohammadi, A., Alinejad-Rokny, H., Sejdinovic, D., . . . Abbasnejad, E. (2024). Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian
Neural Networks.2024 Doan, B. G., Nguyen, D. Q., Lindquist, C., Montague, P., Abraham, T., Vel, O. D., . . . Ranasinghe, D. C. (2024). On the Credibility of Backdoor Attacks Against Object Detectors in the
Physical World.
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