Guarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method

תקציר מקורי באנגליתarXiv:2607.26634v1 Announce Type: cross Abstract: Organizational digitalization expands cybersecurity risks, making cybersecurity an increasingly important research area in Information Systems (IS). Among these risks, malware has become a pervasive and destructive threat. Byte-based machine learning (ML) methods are widely used for malware detection but remain vulnerable to evasive behaviors that manipulate raw bytes to evade detection. Graph-based methods are less affected by such manipulations because they represent software as program graphs that capture execution behavior. However, they do not explicitly identify cohesive groups of basic blocks that jointly realize meaningful program behaviors, nor do they learn sufficiently expressive program graph representations for accurate detecti