78 citations · 161 across the 14 of their papers we have counts for
14 papers
Bayesian Learned Models Can Detect Adversarial Malware For Free
Bao Gia Doan, Dang Quang Nguyen, Paul Montague +6
The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is…
Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks
Huihui Gong, Minjing Dong, Siqi Ma +3
Adversarial training serves as one of the most popular and effective methods to defend against adversarial perturbations. However, most defense mechanisms only consider a single ty…
Stealthy Physical Masked Face Recognition Attack via Adversarial Style Optimization
Huihui Gong, Minjing Dong, Siqi Ma +3
Deep neural networks (DNNs) have achieved state-of-the-art performance on face recognition (FR) tasks in the last decade. In real scenarios, the deployment of DNNs requires taking…
Quantum-Inspired Machine Learning: a Survey
Larry Huynh, Jin Hong, Ajmal Mian +3
Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within cla…
SplITS: Split Input-to-State Mapping for Effective Firmware Fuzzing
Guy Farrelly, Paul Quirk, Salil S. Kanhere +2
Ability to test firmware on embedded devices is critical to discovering vulnerabilities prior to their adversarial exploitation. State-of-the-art automated testing methods rehost f…
Data-Driven Intelligence can Revolutionize Today's Cybersecurity World: A Position Paper
Iqbal H. Sarker, Helge Janicke, Leandros Maglaras +1
As cyber threats evolve and grow progressively more sophisticated, cyber security is becoming a more significant concern in today's digital era. Traditional security measures tend…