Apk2Img4AndMal: Android Malware Detection Framework Based on Convolutional Neura Network

Kural O. E., Şahin D. Ö., Akleylek S., Kılıç E., Ömüral M.

6th International Conference on Computer Science and Engineering, UBMK 2021, Ankara, Turkey, 15 - 17 September 2021, pp.731-734 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ubmk52708.2021.9558983
  • City: Ankara
  • Country: Turkey
  • Page Numbers: pp.731-734
  • Keywords: Android malware, Android malware detection, APK to image, Convolutional neural network, Deep learning, Malware image
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

In this study, the Apk2Img4AndMal framework, which provides information about the application without the need for static or dynamic attributes, is recommended. The proposed framework reads APK files in binary format and converts them to grayscale images. In the classification phase of the framework, the convolutional neural network (CNN) is used, which gives successful results in image classification. In this way, the required features are obtained through a CNN. Therefore, there is also no feature extraction phase as other dynamic or static analysis-based frameworks. This property is the most important advantage of the Apk2Img4AndMal framework. The proposed framework is tested with 24588 Android malware and 3000 benign applications. The highest performance achieved in the study is up to 94%, according to the accuracy metric.