A Lightweight and Multi-Stage Approach for Android Malware Detection Using Non-Invasive Machine Learning Techniques - Leonardo da Costa; Vitor Moia (IEEE Access, 2023)
Android has been a constant target of cybercriminals that try to attack one of the most used operating systems, commonly using malicious applications (denominated malware) that, once installed on a device, can harm users in several ways. Existing malware detection solutions are usually invasive as they obtain classification features by performing reserve engineering, decompilation, or disassembly of the analyzed application, which infringes licenses and terms of use of applications. In addition, these solutions often employ a single machine learning (ML) model to detect various types of malware, resulting in several false alarms. In this context, we propose an approach to detect Android malware consisting of a set of specific-type detectors in which each one performs a multi-stage analysis, based on rules and ML techniques, in different phases of the application cycle (before and after its installation). Our approach also differs from state-of-the-art solutions by being non-invasive, since it leverages a process to obtain application’s features that does not infringe licenses and terms of use of applications. In addition, according to experiments performed on a real Android smartphone, our proposal presents the following additional advantages over state-of-the-art solutions: a more efficient process to classify applications that is three times faster and requires ten times less CPU usage in some cases (saving device energy); and a better detection performance, with higher balanced accuracy, nine times less false positive cases, and ten times less false negative cases.