ComputingComputer ScienceCybersecurityMachine LearningMobile ComputingMobile SecurityMalware ClassificationMobile Device SecurityMobile PrivacyAndroid Application AnalysisElectronic ComputersMachine Learning For Malware ClassificationMachine Learning-based Malware DetectionMalicious App DetectionMobile MalwareMobile Malware DetectionPermission-based Classification
Mobile devices, especially Android smartphones, are widely used and often receive unverified apps that can contain malware capable of stealing user privacy. The study proposes a framework to detect Android malware and improve market organization. The framework uses machine‑learning classifiers on permission‑based features and events extracted from apps to distinguish goodware from malware.
Mobile devices have become popular in our lives since they offer almost the same functionality as personal computers. Among them, Android-based mobile devices had appeared lately and, they were now an ideal target for attackers. Android-based smartphone users can get free applications from Android Application Market. But, these applications were not certified by legitimate organizations and they may contain malware applications that can steal privacy information for users. In this paper, a framework that can detect android malware applications is propos ed to help organizing Android Market. The proposed framework intends to develop a machine learning-based malware detection system on Android to detect malware applications and to enhance security and privacy of smartphone users. This system monitors various permissionbased features and events obtained from the android applications, and analyses these features by using machine learning classifiers to classify whether the application is goodware or malware.
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