APKOWL: An Automatic Approach to Enhance the Malware Detection

Abstract

Malicious software (malware) can steal passwords, leak details, and generally cause havoc with users’ accounts. Most of the current malware detection techniques are designed to detect malware at the code level of the software, where it is actually infected and causes damage. Additionally, current malware detection techniques at the design level are done manually or semi-automatically. This research aims to enhance these methods to detect malware at the design level automatically with a big dataset. The proposed method presents an automatic system for detecting SMS (Short Message Service) malware at the design which is called APKOWL. It is based on reverse engineering of the mobile application and then automatically builds OWL (web ontology Language) ontology. The proposed system is implemented in python and Protégé, and its performance has been tested and evaluated on samples of android mobile applications including 3,904 malware and 3,200 benign samples. The experimental results successfully verify the effectiveness of the proposed method because it has good performance in detecting SMS malware at the software design level. The proposed method obtained an accuracy of 97%, precision of 97.5%, and recall of 99%, outperforming the compared model in all performance metrics.