Graph Based Malware Analysis API

In the realm of cybersecurity malware detection and classification remain critical, challenges as adversarial techniques grow increasingly sophisticated. Graph Based Malware Analysis API is designed to improve the accuracy and efficiency of malware detection by utilizing graph structure to represent and analyze malicious behaviors. By leveraging Cuckoo Sandbox, malicious files are executed in a controlled environment, generating detailed reports on file behaviors and system interactions. These reports are transformed into graph representations, capturing intricate relationships and dependencies among various system components affected by malware. The resulting graphs serve as input for machine learning models trained to classify malware types, enhancing detection rates and reducing instances of “Unknown” classifications.

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Publication Details

Published
2025-04-24
DOI
https://doi.org/10.1109/icoei65986.2025.11013332
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Graph Based Malware Analysis API

R. Keerthana, Rohith Vaidya K, Harini Sri M, Harsha Vardhan G et al.
Advanced Malware Detection Techniques
article

Graph Based Malware Analysis API

R. Keerthana, Rohith Vaidya K, Harini Sri M, Harsha Vardhan G, N Digvijay
article en

Abstract

In the realm of cybersecurity malware detection and classification remain critical, challenges as adversarial techniques grow increasingly sophisticated. Graph Based Malware Analysis API is designed to improve the accuracy and efficiency of malware detection by utilizing graph structure to represent and analyze malicious behaviors. By leveraging Cuckoo Sandbox, malicious files are executed in a controlled environment, generating detailed reports on file behaviors and system interactions. These reports are transformed into graph representations, capturing intricate relationships and dependencies among various system components affected by malware. The resulting graphs serve as input for machine learning models trained to classify malware types, enhancing detection rates and reducing instances of “Unknown” classifications.

PES University (IN)
Openalex Percentile: Top 87%
Advanced Malware Detection Techniques
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