Image Based Android Malware Detection Models Using Static And Dynamic Features

Image-based Android Malware Detection Models using Static and Dynamic Features

Link: https://link.springer.com/chapter/10.1007/978-3-030-96308-8_120

Authors: Hemant Rathore, B Raja Narasimhan, Sanjay K Sahay, Mohit Sewak Publication date: 2022 Conference: International Conference on Intelligent Systems Design and Applications Pages: 1292-1305 Publisher: Springer, Cham

Description Today smartphones are an indispensable part of our everyday activities and store a plethora of sensitive as well as personal information. However, this information is an attractive target of malware designers that can be validated with an ever-increasing number of smartphone malware. Recently researchers explored deep learning for detecting android malware and have seen encouraging results. In this paper, we propose an effective image-based android malware detection system. We used both static and dynamic analysis of android applications to extract six different features: intent, opcode, permission from static analysis, and unigram, bigram, trigram from system call log using dynamic analysis. Then, we proposed a custom malware detection model (MalCNN) that uses static features and achieved accuracy and AUC of and 0.99 respectively in malware detection. We also explored MobileNetV2 based malware detection models for dynamic features that achieved accuracy and AUC of and 0.99 respectively in malware detection. Our experimental results show that image representation of static or dynamic features can be used for effective malware detection.