A study of feature selection methods for android malware detection

Abstract

Abstract Feature Selection (FS) provides a vital role in the android malware detection system. The researchers have presented FS methods and tested them on benchmark datasets, including static types of features extracted from applications. This paper studies FS methods used in traditional android malware detection systems. These FS methods are implemented on the benchmark datasets such as Genome project, Google PlayStore, AndroZoo, and Drebin consist of static types of extracted features from applications. These traditional methods are studied and implemented on the latest dataset, such as CIC-MalDroid2020 dataset, which includes the latest types of malware and 470 dynamic types of features. The experimentation is performed on CIC-MalDroid2020 dataset with the Random Forest (RF) classifier using traditional FS methods, and performance is compared with the original feature set. Finally, the investigation with the RF classifier on CIC-MalDroid2020 dataset using the obtained 80 features from 470 original features by the ReliefF method produces higher precision of 97.4647% and a lower FAR of 0.1409 for malware detection.