Assessing the opportunity of combining state-of-the-art Android malware detectors - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Empirical Software Engineering Année : 2022

Assessing the opportunity of combining state-of-the-art Android malware detectors

Résumé

Research on Android malware detection based on Machine learning has been prolific in recent years. In this paper, we show, through a large-scale evaluation of four state-of-the-art approaches that their achieved performance fluctuates when applied to different datasets. Combining existing approaches appears as an appealing method to stabilise performance. We therefore proceed to empirically investigate the effect of such combinations on the overall detection performance. In our study, we evaluated 22 methods to combine feature sets or predictions from the state-of-the-art approaches. Our results showed that no method has significantly enhanced the detection performance reported by the state-of-the-art malware detectors. Nevertheless, the performance achieved is on par with the best individual classifiers for all settings. Overall, we conduct extensive experiments on the opportunity to combine state-of-the-art detectors. Our main conclusion is that combining state-of-the-art malware detectors leads to a stabilisation of the detection performance, and a research agenda on how they should be combined effectively is required to boost malware detection. All artefacts of our large-scale study (i.e., the dataset of ∼0.5 million apks and all extracted features) are made available for replicability.
Fichier principal
Vignette du fichier
s10664-022-10249-9.pdf (1.97 Mo) Télécharger le fichier
Origine : Publication financée par une institution

Dates et versions

hal-03925375 , version 1 (23-01-2023)

Identifiants

Citer

Nadia Daoudi, Kevin Allix, Tegawendé Bissyandé, Jacques Klein. Assessing the opportunity of combining state-of-the-art Android malware detectors. Empirical Software Engineering, 2022, 28 (2), pp.22. ⟨10.1007/s10664-022-10249-9⟩. ⟨hal-03925375⟩
37 Consultations
30 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More