2 citations · 2 across the 6 of their papers we have counts for
6 papers
Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
Ahmed Sabbah, Radi Jarrar, Samer Zein +1
Despite outstanding results, machine learning-based Android malware detection models struggle with concept drift, where rapidly evolving malware characteristics degrade model effec…
Understanding Concept Drift with Deprecated Permissions in Android Malware Detection
Ahmed Sabbah, Radi Jarrar, Samer Zein +1
Permission analysis is a widely used method for Android malware detection. It involves examining the permissions requested by an application to access sensitive data or perform pot…
Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing
Ilkin Sevgi Isler, David Mohaisen, Curtis Lisle +2
Reliable tumor segmentation in thoracic computed tomography (CT) remains challenging due to boundary ambiguity, class imbalance, and anatomical variability. We propose an uncertain…
SoK: Can Fully Homomorphic Encryption Support General AI Computation? A Functional and Cost Analysis
Jiaqi Xue, Xin Xin, Wei Zhang +11
Artificial intelligence (AI) increasingly powers sensitive applications in domains such as healthcare and finance, relying on both linear operations (e.g., matrix multiplications i…
Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis
Ahod Alghureid, David Mohaisen
This paper explores the vulnerability of machine learning models, specifically Random Forest, Decision Tree, and K-Nearest Neighbors, to very simple single-feature adversarial atta…
Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning
Dongjie Wang, Kunpeng Liu, David Mohaisen +3
Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, Spatial Representation Learning (SRL) use…