  Malware represents a significant security concern in today's digital
landscape, as it can destroy or disable operating systems, steal sensitive user
information, and occupy valuable disk space. However, current malware detection
methods, such as static-based and dynamic-based approaches, struggle to
identify newly developed (``zero-day") malware and are limited by customized
virtual machine (VM) environments. To overcome these limitations, we propose a
novel malware detection approach that leverages deep learning, mathematical
techniques, and network science. Our approach focuses on static and dynamic
analysis and utilizes the Low-Level Virtual Machine (LLVM) to profile
applications within a complex network. The generated network topologies are
input into the GraphSAGE architecture to efficiently distinguish between benign
and malicious software applications, with the operation names denoted as node
features. Importantly, the GraphSAGE models analyze the network's topological
geometry to make predictions, enabling them to detect state-of-the-art malware
and prevent potential damage during execution in a VM. To evaluate our
approach, we conduct a study on a dataset comprising source code from 24,376
applications, specifically written in C/C++, sourced directly from
widely-recognized malware and various types of benign software. The results
show a high detection performance with an Area Under the Receiver Operating
Characteristic Curve (AUROC) of 99.85%. Our approach marks a substantial
improvement in malware detection, providing a notably more accurate and
efficient solution when compared to current state-of-the-art malware detection
methods.
