AMD-FCG: An Enhanced Function Call Graph Dataset with Integrated Topological Features for Malware Detection and Classification

2026-06-04

AI Summary

This study addresses the challenge of efficiently and accurately identifying complex malware families, which traditional methods struggle to achieve. To this end, the authors propose an enhanced static analysis–based approach for modeling function call graphs (FCGs), introducing AMD-FCG—a large-scale, high-quality dataset that uniquely integrates diverse topological graph features from both malicious and benign applications. Notably, AMD-FCG eliminates the need for dynamic analysis, thereby substantially simplifying the detection pipeline while providing structured inputs suitable for machine learning models. Experimental results demonstrate that AMD-FCG significantly improves the accuracy and robustness of malware detection and classification, offering a reliable data foundation and toolset for advancing cybersecurity defenses.

Abstract

As malware illustrates a complex structure and behavior, detection of these has been a significant challenge in the domain of cybersecurity along with related services in daily life. So, it becomes crucial to have a reliable and adaptive solution to address the issue. Among the several detection methods developed over the years, one of the most reliable ones is studying and analyzing the structural and behavioral patterns of malware. These patterns of sophisticated malware can be obtained with the help of Function Call Graphs (FCGs). However, to effectively cover numerous groups of families of malware, it is required to have a sufficiently large dataset for the system to operate on. In order to ensure accuracy and robustness of the system, the dataset should comprise samples of different malwares and a benign application for secure execution of the detection process. This paper introduces AMD-FCG, an enhanced Function Call Graph dataset integrated with topological features of malwares. The framework enhances the detection procedure, streamlining the workflow for cybersecurity professionals and also eliminating the need for dynamic analysis and extensive processing. Therefore, it can be used to develop and deploy more efficient and innovative malware detection systems.

Problem

• malware detection
• Function Call Graph
• dataset
• topological features
• malware classification

Innovation

• Function Call Graph
• Topological Features
• Malware Detection
• Static Analysis
• Enhanced Dataset