Graph Neural Networks for Malware Detection

A Novel Approach to Cybersecurity

Abstract

In the ever-evolving landscape of cybersecurity, traditional methods for malware detection are increasingly challenged by sophisticated threats that exploit conventional detection techniques. This paper explores the application of Graph Neural Networks (GNNs) for malware detection, emphasizing their ability to uncover hidden relationships between data points and enhance the identification of malicious software. By leveraging the structural information inherent in data, GNNs offer a novel approach to understanding complex interactions within malware behavior and its associated artifacts. This research introduces a framework for implementing GNNs in the context of malware detection, detailing their architecture, operational mechanisms, and advantages over traditional methods. Through a series of experiments, the efficacy of GNNs is demonstrated, showcasing their improved performance in classifying and detecting malware samples compared to established machine learning techniques. The findings indicate that GNNs not only improve detection accuracy but also reduce false positive rates, contributing to more effective cybersecurity defenses. This paper concludes with a discussion on the implications of GNNs in future cybersecurity applications and the potential for further research in this promising domain.