Android Malware Detection Using BERT

In this paper, we propose two empirical studies to (1) detect Android malware and (2) classify Android malware into families. We rst (1) reproduce the results of MalBERT using BERT models learning with Android application's manifests obtained from 265k applications (vs. 22k for MalBERT) from the AndroZoo dataset in order to detect malware. The results of the MalBERT paper are excellent and hard to believe as a manifest only roughly represents an application, we therefore try to answer the following questions in this paper. Are the experiments from MalBERT reproducible? How important are Permissions for mal- ware detection? Is it possible to keep or improve the results by reducing the size of the manifests? We then (2) investigate if BERT can be used to classify Android malware into families. The results show that BERT can successfully di erentiate malware/goodware with 97% accuracy. Further- more BERT can classify malware families with 93% accuracy. We also demonstrate that Android permissions are not what allows BERT to successfully classify and even that it does not actually need it