Android Malware Detection Using Graphical Technique
Monish Kumar Sahu, Rahul Gupta · 2024
Android is the most popular operating system for mobile devices which has dominated the smartphone industry. Malware is software that aims to damage computer networks, expose personal data, allow unwanted access, steal data from users, or inadvertently compromise users’ computer security and privacy. Traditional signature-based malware detection approaches have been in use for a long time. Yet their technology falls far short of being completely secure. Modern malware detection tools are used by major mobile application distributors, official stores, and marketplaces to analyse uploaded programs and eliminate any dangerous ones. Unfortunately, until they are taken off the market, malicious software has a long window of opportunity. Numerous apps can get past these detectors and continue to be downloaded and installed on devices by users everywhere in the market. In this paper, we studied different research papers based on graphical techniques and learned about static and dynamic malware detection approaches. We presented a new and unique method for detection of Android malware that uses graph neural networks and app-similarity graph. The proposed model provided reasonable accuracy and hence served to aid and maintain a user-safe environment.