Detecting Malware in Android Applications using XGBoost
Date Issued
2023-07
Author(s)
Kitanovski, Aleksandar
Mihajloska Trpcheska, Hristina
Abstract
The omnipresence of Android devices and the
amount of sensitive information kept in them makes detecting
malware in Android applications crucial. In this paper, the
efficacy of using machine learning models for the purpose of
malware detection in Android applications was examined, and
several XGBoost models were developed and compared - each
with a distinct feature set. We used the f1 score, precision, recall,
confusion matrices, and precision-recall curves to compare the
models. Accuracy was not considered since we needed a balanced
dataset. One of the models we developed, which used all the
available features in the dataset, had encouraging results with
high precision and recall.
amount of sensitive information kept in them makes detecting
malware in Android applications crucial. In this paper, the
efficacy of using machine learning models for the purpose of
malware detection in Android applications was examined, and
several XGBoost models were developed and compared - each
with a distinct feature set. We used the f1 score, precision, recall,
confusion matrices, and precision-recall curves to compare the
models. Accuracy was not considered since we needed a balanced
dataset. One of the models we developed, which used all the
available features in the dataset, had encouraging results with
high precision and recall.
Subjects
File(s)![Thumbnail Image]()
Loading...
Name
CIIT2023_paper_10.pdf
Size
8.97 MB
Format
Adobe PDF
Checksum
(MD5):3c4f8751eb0877cdfbd7b4c372c2ffde