21 citations · 29 across the 14 of their papers we have counts for
5 papers · 1 filter
Breaking the Illusion of Security via Interpretation: Interpretable Vision Transformer Systems under Attack
Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo +2
Vision transformer (ViT) models, when coupled with interpretation models, are regarded as secure and challenging to deceive, making them well-suited for security-critical domains s…
From Attack to Defense: Insights into Deep Learning Security Measures in Black-Box Settings
Firuz Juraev, Mohammed Abuhamad, Eric Chan-Tin +2
Deep Learning (DL) is rapidly maturing to the point that it can be used in safety- and security-crucial applications. However, adversarial samples, which are undetectable to the hu…
SHIELD: Thwarting Code Authorship Attribution
Mohammed Abuhamad, Changhun Jung, David Mohaisen +1
Authorship attribution has become increasingly accurate, posing a serious privacy risk for programmers who wish to remain anonymous. In this paper, we introduce SHIELD to examine t…
Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning
Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo +2
Deep learning methods have gained increased attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper us…
A Deep Learning-based Fine-grained Hierarchical Learning Approach for Robust Malware Classification
Ahmed Abusnaina, Mohammed Abuhamad, Hisham Alasmary +5
The wide acceptance of Internet of Things (IoT) for both household and industrial applications is accompanied by several security concerns. A major security concern is their probab…