5 papers
Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness
Bao Gia Doan, Shuiqiao Yang, Paul Montague +6
We present a new algorithm to train a robust malware detector. Modern malware detectors rely on machine learning algorithms. Now, the adversarial objective is to devise alterations…
Didact: A Cross-Domain Capability Discovery System for Defence
Aarya Bodhankar, Aditya Joshi, Bao Gia Doan +3
Policymakers in defence and defence-aligned sectors must monitor rapidly evolving research alongside sector priorities relevant to operational and strategic needs. In practice, the…
A Benchmark Construction and Evaluation Framework for Specialist Domains: Case Study on Defense-related Documents
Bao Gia Doan, Aditya Joshi, Pantelis Elinas +4
RAG-based question-answering (QA) in specialist domains faces a cold-start problem: lack of evaluative benchmarks and absence of labeled data for post-training. We present DoRA (Do…
Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks
Bao Gia Doan, Afshar Shamsi, Xiao-Yu Guo +6
Computational complexity of Bayesian learning is impeding its adoption in practical, large-scale tasks. Despite demonstrations of significant merits such as improved robustness and…
On the Credibility of Backdoor Attacks Against Object Detectors in the Physical World
Bao Gia Doan, Dang Quang Nguyen, Callum Lindquist +7
Object detectors are vulnerable to backdoor attacks. In contrast to classifiers, detectors possess unique characteristics, architecturally and in task execution; often operating in…