This dataset was produced as a part of my PhD research on Android malware detection using Multimodal Deep Learning. It contains raw data (DEX grayscale images), static analysis data (Android Intents & Permissions), and dynamic analysis data (system call sequences). For the conference research paper, please refer to https://sbic.org.br/eventos/cbic_2021/cbic2021-32/

Citations:

Dataset Files

You must be an IEEE Dataport Subscriber to access these files. Subscribe now or login.

[1] Angelo Oliveira, Renato Sassi, "Malware Analysis Datasets: Chimera Multimodal Deep Learning Android Malware Detection Method", IEEE Dataport, 2021. [Online]. Available: http://dx.doi.org/10.21227/1wdz-2d93. Accessed: May. 18, 2022.
@data{1wdz-2d93-21,
doi = {10.21227/1wdz-2d93},
url = {http://dx.doi.org/10.21227/1wdz-2d93},
author = {Angelo Oliveira; Renato Sassi },
publisher = {IEEE Dataport},
title = {Malware Analysis Datasets: Chimera Multimodal Deep Learning Android Malware Detection Method},
year = {2021} }
TY - DATA
T1 - Malware Analysis Datasets: Chimera Multimodal Deep Learning Android Malware Detection Method
AU - Angelo Oliveira; Renato Sassi
PY - 2021
PB - IEEE Dataport
UR - 10.21227/1wdz-2d93
ER -
Angelo Oliveira, Renato Sassi. (2021). Malware Analysis Datasets: Chimera Multimodal Deep Learning Android Malware Detection Method. IEEE Dataport. http://dx.doi.org/10.21227/1wdz-2d93
Angelo Oliveira, Renato Sassi, 2021. Malware Analysis Datasets: Chimera Multimodal Deep Learning Android Malware Detection Method. Available at: http://dx.doi.org/10.21227/1wdz-2d93.
Angelo Oliveira, Renato Sassi. (2021). "Malware Analysis Datasets: Chimera Multimodal Deep Learning Android Malware Detection Method." Web.
1. Angelo Oliveira, Renato Sassi. Malware Analysis Datasets: Chimera Multimodal Deep Learning Android Malware Detection Method [Internet]. IEEE Dataport; 2021. Available from : http://dx.doi.org/10.21227/1wdz-2d93
Angelo Oliveira, Renato Sassi. "Malware Analysis Datasets: Chimera Multimodal Deep Learning Android Malware Detection Method." doi: 10.21227/1wdz-2d93