MalGraphIQ: A tool for generating behavior representations of malware execution traces

Understanding and interpreting malware behavior remains an open challenge in the field of cybersecurity. The dynamic analysis of malware execution traces has emerged as a promising approach for discovering behavioral insights that allow the visual explanation of malware activity. MalGraphIQ is an open-source tool for the analysis and visualization of malware behavior. It is based on a structured and hierarchical taxonomy of API-based behavior patterns, which facilitates the interpretation of malware objectives, strategies, and low-level interactions with the attacked system. These interpretations support the comparative analysis of collections of suspicious programs, particularly across malware families and types, enhancing security research, malware triage, and the development of behavior-aware detection systems.

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Publication Details

Journal
SoftwareX
Published
2025-10-22
DOI
https://doi.org/10.1016/j.softx.2025.102407
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

MalGraphIQ: A tool for generating behavior representations of malware execution traces

Razvan Raducu, Ricardo J. Rodríguez, Pedro J. J. Alvarez
SoftwareX
Advanced Malware Detection Techniques
article

MalGraphIQ: A tool for generating behavior representations of malware execution traces

Razvan Raducu, Ricardo J. Rodríguez, Pedro J. J. Alvarez
article en

Abstract

Understanding and interpreting malware behavior remains an open challenge in the field of cybersecurity. The dynamic analysis of malware execution traces has emerged as a promising approach for discovering behavioral insights that allow the visual explanation of malware activity. MalGraphIQ is an open-source tool for the analysis and visualization of malware behavior. It is based on a structured and hierarchical taxonomy of API-based behavior patterns, which facilitates the interpretation of malware objectives, strategies, and low-level interactions with the attacked system. These interpretations support the comparative analysis of collections of suspicious programs, particularly across malware families and types, enhancing security research, malware triage, and the development of behavior-aware detection systems.

SoftwareXVol. 32
Universidad de Zaragoza (ES)
European Commission, Gobierno de Aragón, Instituto Nacional de Ciberseguridad, Agencia Estatal de Investigación
Openalex Percentile: Top 63%
Advanced Malware Detection Techniques
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MalGraphIQ: A tool for generating behavior representations of malware execution traces — Razvan Raducu, Ricardo J. Rodríguez, et al. · SoftwareX (2025) | TGRS Research Map | TGRS