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A study on the role of feature selection for malware detection on Android applications

Palma, C.R.P. ; Ferreira, A. ; Figueiredo, M. A. T.

A study on the role of feature selection for malware detection on Android applications, Proc RECPAD Portuguese Conf. on Pattern Recognition - RecPad RECPAD, Coimbra, Portugal, Vol. , pp. - , October, 2023.

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Abstract
The presence of malicious software (malware) in Android applications (apps) has harmful or irreparable consequences to the user and/or the device. Despite the protections provided by app stores, malware keeps growing in both sophistication and diffusion. This paper explores the use of machine learning (ML) and feature selection (FS) approaches to detect malware in Android applications using public domain datasets. We resort
to the relevance-redundancy FS (RRFS) filter method using the unsupervised mean-median (MM) and the supervised Fisher ratio (FR) relevance measures. Our approach is able to reduce the dimensionality of the data, improving the experimental results of the baseline model, and identifies the most decisive features to classify an app as malware.