A malware classification method based on directed API call relationships.

ABSTRACT: In response to the growing complexity of network threats, researchers are increasingly turning to machine learning and deep learning techniques to develop advanced models for malware detection. Many existing methods that utilize Application Programming Interface (API) sequence instructions for malware classification often overlook the structural information inherent in these sequences. While some approaches consider the structure of API calls, they typically rely on the Graph Convolutional Network (GCN) framework, which tends to neglect the sequential nature of API interactions. To address these limitations, we propose a novel malware classification method that leverages the directed relationships within API sequences. Our approach models each API sequence as a directed graph, incorporatin

SUBMITTER: Ma C 

PROVIDER: S-EPMC11913307 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature