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Título
A review of: Optimal feature configuration for dynamic malware detection
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Es parte de
Investigación en Ciberseguridad. Actas de las VII Jornadas Nacionales (7º.2022.Bilbao)
Cita Bibliográfica
Escudero García, D., & DeCastro García, N. (2022). A review of: Optimal Feature Configuration forDynamic Malware Detection. En J. M. de Fuentes, L. González Manzano, J. C. Sancho Núñez, A. Ayerbe, & L. M. Alcocer Escalante (eds.), Investigación en Ciberseguridad Actas de las VII Jornadas Nacionales (7º.2022.Bilbao) (pp. 277-278). Fundación Tecnalia Research and Innovation.
Editorial
Fundación Tecnalia Research and Innovation
Fecha
2022-06
Resumen
[EN] Applying machine learning techniques to malware detection is a common approach to try to overcome the limitations of signature-based methods. However, it is difficult to engineer a set of features that characterizes the samples properly, especially when various file types may be a vector of infection. In this work, we configure several feature sets for dynamic malware detection extracted from API calls, network activity, signatures
from the Cuckoo sandbox report, and some interactions with the file system and registry. We test combinations of these feature sets to ascertain whether they are good enough to distinguish between benign and malicious samples from a dataset containing several file types, obtained from public sources. The datasets present class imbalance to evaluate the model performance on more realistic scenarios in which not many malware samples are available
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- This work was partially supported by the Spanish National Cybersecurity Institute (INCIBE) under contract Art.83, key: X54. We thank the reviewers and the editor for their helpful comments that greatly improved the manuscript. Also, we thank Ángel Luis Muñoz Castañeda for his advice regarding the manuscript.
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