Malware detection using image-based features and machine learning methods

Abstract

As Android devices occupy more of people's lives, they have also become a target of malicious software. It is important to detect malicious software and to prevent the losses and damages that may arise from this software. For this purpose, various studies are being carried out about malware detection. Recently, imagebased methods and machine learning studies have come to the fore. In these studies, binary files used in static and dynamic analysis are converted into image files. Global and local features extracted from the images are classified by various machine learning methods. In this study, global features were extracted on the malimg dataset and a feature matrix (2000, 532) long was obtained. The obtained features were classified using machine learning methods (LR, LDA, K-NN, CART, RF, NB, SVM). The results were evaluated using the K-fold crossover validation method, and a highest accuracy rate of 96.72% was obtained with K-NN and 97.44% with RF. This study contributes to the literature by reaching a higher accuracy value compared to other studies on the same data set.