A New Approach to Improve Mobile Network’s Security Through Android Malware Detection Utilizing Static Analysis

The security of the mobile devices has become a major issue since hackers target them through malwares in order to harm the systems or gather sensitive information and get access to the systems remotely. Recently, new ways have been introduced to confront malwares and other viruses. Two main techniques for recognizing malwares are dynamic analysis and static analysis. This paper proposes a new method using the static analysis to help improve the accuracy of the malwares in detecting threats faster and with lower processing time. For this purpose, our suggested method has utilized the android application’s main components to recognize the malwares using the machine learning algorithms. Furthermore, our method has used the feature selection algorithms to reduce the processing overload and to enhance the speed and accuracy. Our method have used the following components as the classification features in our suggested algorithms: API calls, Intents, network address and IPs, services and provider, activities and permissions. In addition to these individual features, our method has also employed complex features to improve malware recognition. We have used 123,446 software and 5,561 malwares to evaluate the accuracy and the precision of the suggested method, demonstrating to be 99.4 percent.