Evaluation of Machine Learning Techniques for Malware Detection

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-10-05T16:33:59Z
Fecha de publicación2023-01-01
ResumenCurrently, there is a security breach in technological systems, attacks on computers and mobile devices through malicious software (also called malware) continue to increase. Malicious software attacks occur at all levels and all types of devices, affecting computer users, corporations, industry, and government. Therefore, the detection of malware continues to be a challenge in computer science. Algorithms based on machine learning and deep learning are being used recently to build software solutions that allow the identification of malicious data in real-time. In this paper, we evaluate three classic machine learning algorithms and one neural network-based algorithm for malware detection and classification using different public data sets. The support vector machine (SVM) and the J48 decision tree (DT) algorithms obtain the best results in all the experiments with values equal to the results available in state-of-the-art. The extreme learning machine (ELM) algorithm obtains very acceptable results in experimentation. The results successfully validate the effectiveness of the implemented algorithms, improving the generalization performance of detecting a new instance of malicious software.es_ES
Doihttps://doi.org/10.1007/978-3-031-08246-7_6es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/5743
Idiomaenes_ES
EditorialSpringer Science and Business Media Deutschland GmbHes_ES
RelaciónIntelligent Systems Reference Libraryes_ES
URL relacionadohttps://doi.org/10.1007/978-3-031-08246-7_6es_ES
DerechosAcceso restringido / Suscripción (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_16eces_ES
FuenteIntelligent Systems Reference Library
TítuloEvaluation of Machine Learning Techniques for Malware Detectiones_ES
TipoCapítulo Libroes_ES
ArbitradoHa sido Arbitradoes_ES
AutorMata-Torres, Jonathan Alfonso
AutorTello-Leal, Edgar
AutorHernandez-Resendiz, Jaciel David
AutorRamirez-Alcocer, Ulises Manuel
AutorMata-Torres, Jonathan Alfonsoes_ES
AutorTello-Leal, Edgares_ES
AutorHernandez-Resendiz, Jaciel Davides_ES
AutorRamirez-Alcocer, Ulises Manueles_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Rango de páginas121-140es_ES
URL relacionadahttps://doi.org/10.1007/978-3-031-08246-7_6
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen226es_ES

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