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Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence.

, , , , , , , , and . IJCNN, page 1-8. IEEE, (2020)

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Event sequence mining to develop profiles for computer forensic investigation purposes.. ACSW, volume 54 of CRPIT, page 145-153. Australian Computer Society, (2006)Removing Undesirable Concepts in Text-to-Image Generative Models with Learnable Prompts., , , , , and . CoRR, (2024)EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection., , , , , , , and . CoRR, (2024)Local Intrinsic Dimensionality Signals Adversarial Perturbations., , , , , and . CDC, page 6118-6125. IEEE, (2022)Investigative Profile Analysis With Computer Forensic Log Data Using Attribute Generalisation., , and . AusDM, page 17-28. University of Technology Sydney, Australia, (2002)Opportunities for Knowledge Discovery in Spatio-Temporal Information Systems., and . Australas. J. Inf. Syst., (1998)Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness., , , , , , , , and . AAAI, page 14783-14791. AAAI Press, (2023)Investigative Profiling with Computer Forensic Log Data and Association Rules., and . ICDM, page 11-18. IEEE Computer Society, (2002)Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness., , , , , , and . AAAI, page 6831-6839. AAAI Press, (2021)Bayesian Learned Models Can Detect Adversarial Malware for Free., , , , , , , , and . ESORICS (1), volume 14982 of Lecture Notes in Computer Science, page 45-65. Springer, (2024)