AMD-FCG: An Enhanced Function Call Graph Dataset with Integrated Topological Features for Malware Detection and Classification
arXiv:2606.06815v1 Announce Type: cross 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, t
The increasing sophistication of malware and the limitations of traditional detection methods necessitate adaptive, reliable solutions, pushing research into advanced analytical techniques like Function Call Graphs.
Improved malware detection and classification directly enhance cybersecurity defenses, protecting critical infrastructure, personal data, and economic stability in an increasingly digital world.
This research introduces an enhanced dataset and methodology that could lead to more robust and accurate AI-driven malware analysis, reducing evasion rates and detection latency.
- · Cybersecurity firms
- · AI/ML researchers
- · Critical infrastructure operators
- · Governments
- · Malware developers
- · Organized cybercrime groups
Increased efficacy of AI-driven cybersecurity tools for threat detection.
Reduced incidence and impact of cyberattacks on businesses and individuals.
Enhanced trust in digital systems and accelerated adoption of connected technologies due to improved security posture.
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