TY - JOUR
T1 - Revisión Sistemática de la Literatura
T2 - Machine Learning para la Detección de Ransomware en Dispositivos Móviles
AU - Castro-Salaverry, Cristian R.
AU - Bravo-Huivin, Elizabeth K.
AU - Cieza-Mostacero, Segundo E.
N1 - Publisher Copyright:
© 2022, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.
PY - 2022
Y1 - 2022
N2 - In the year 2022, mobile devices became the target of many Ransomware attacks preventing users from accessing their files. The purpose of this article was to present the research preferences in the detection of this type of Malware based on Machine Learning. For this reason, a systematic review was carried out that provided indicators to measure detection accuracy, prevention measures and statistical data that show the most used algorithms for tracking Ransomware on mobile devices, such as: Support Vector Machine (24%), Random Forest Regression (18%), k-NN (15%), and J48 Decision Tree (12%).
AB - In the year 2022, mobile devices became the target of many Ransomware attacks preventing users from accessing their files. The purpose of this article was to present the research preferences in the detection of this type of Malware based on Machine Learning. For this reason, a systematic review was carried out that provided indicators to measure detection accuracy, prevention measures and statistical data that show the most used algorithms for tracking Ransomware on mobile devices, such as: Support Vector Machine (24%), Random Forest Regression (18%), k-NN (15%), and J48 Decision Tree (12%).
KW - Machine Learning
KW - Ransomware
KW - algorithm
KW - mobile devices
KW - systematic review
UR - http://www.scopus.com/inward/record.url?scp=85159188866&partnerID=8YFLogxK
M3 - Artículo
AN - SCOPUS:85159188866
SN - 1646-9895
VL - 2022
SP - 341
EP - 353
JO - RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
JF - RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
IS - E54
ER -