PermQRDroid: Android malware detection with novel attention layered mini-ResNet architecture over effective permission information image

Using the proposed method, accuracy values of 96.95%, 98.34%, 98.33% and 100% were achieved, respectively, on four datasets containing applications obtained from different sources such as Androzoo, Drebin, Genome and Google Play Store. On the Mix dataset, which is a combination of four datasets, an accuracy value of 96.7% was produced with the proposed method. When 10-fold cross validation was applied to reduce the suggested bias, accuracy values of 97.50%, 98.62%, 98%, 94% and 97.61% were obtained, respectively. The success and durability of the proposed method in different environments have been tested through experiments conducted on different datasets. The results show that the proposed method exhibits better classification performance compared to classical machine learning algorithms, deep learning-based studies using permission information, and similar image-based studies.