DIDroid: Android Malware Classification and Characterization Using Deep Image Learning
Abir Rahali, Arash Habibi Lashkari, Gurdip Kaur, Laya Taheri, François Gagnon, Frédéric Massicotte
Abstract
The unrivaled threat of android malware is the root cause of various security problems on the internet. Although there are remarkable efforts in detection and classification of android malware based on machine learning techniques, a small number of attempts are made to classify and characterize it using deep learning. Detecting android malware in smartphones is an essential target for cyber community to get rid of menacing malware samples. This paper proposes an image-based deep neural network method to classify and characterize android malware samples taken from a huge malware dataset with 12 prominent malware categories and 191 eminent malware families. This work successfully demonstrates the use of deep image learning to classify and characterize android malware with an accuracy of 93.36% and log loss of less than 0.20 for training and testing set.