On the use of machine learning techniques to detect malware in mobile applications
Palma, C.R.P.
;
Ferreira, A.
;
Figueiredo, M. A. T.
On the use of machine learning techniques to detect malware in mobile applications, Proc Inforum - Simpósio de Informática, Porto, Portugal, Vol. , pp. - , September, 2023.
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Abstract
The presence of malicious software (malware), for example
in Android applications (apps), has harmful or irreparable consequences to the user and/or the device. Despite the protections provided by app stores to restrict apps containing malware, it keeps growing in both sophistication and diffusion. In this paper, we explore the use of machine learning (ML) and feature selection (FS) approaches to detect malware
in Android applications using public domain datasets. We focus on different data pre-processing, dimensionality reduction, and classification techniques, assessing the generalisation ability of the learned models. The support vector machine (SVM) and random forest (RF) classifiers achieve the best results, with high accuracy and a low false negative (FN) rate. The performance of ML methods is highly dependent on the dataset and its pre-processing and on FS methods identifying the most
decisive features to classify an app as malware.