PowerGNN: A source code structural and textual awareness approach for identifying malicious PowerShell scripts

To keep pace with the rapid advancements in both the quality and complexity of malware, recent research has extensively employed machine learning (ML) and deep learning (DL) models to detect malicious software, particularly in the widely used Windows system. Despite demonstrating promising accuracy in identifying malware, these models remain vulnerable...

Malware continues to evolve, exposing weaknesses in conventional detectors and motivating realistic adversarial evaluations. Prior RL-based evasion methods often rely on partial model access or feature-level perturbations, limiting realism under strict black-box constraints. We propose xPriMES, a dual-environment reinforcement learning framework that generates functionality-preserving binary mutations for malware evasion in...

Android malware detection by using graph optimization of static features based on pre-trained language models