2 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…