Android Malware Classification Using Deep Learning CNN with Co-occurrence Matrix Feature

Abstract

Recently, deep learning has been widely applying to speech and image recognition. Convolutional neural network (CNN) is one of the main categories to do image classifications with very high accuracy. In Android malware classification field, many works have been trying to convert Android malwares into “images” to make them well-matched with the CNN input to take advantage of the CNN model. The performance, however, is not significantly improved because simply converting malwares into images may lack several important features of the malwares. This paper proposes a method for improving the feature set of Android malware classification based on co-concurrence matrix (co-matrix). The co-matrix is established based on a list of raw features extracted from .apk files. The proposed feature can take the advantage of CNN while remaining important features of the Android malwares. Experimental results of CNN model conducted on a very popular Android malware dataset, Drebin, prove the feasibility of our proposed co-matrix feature.