The experiment results show that only use dangerous permissions or the number of used permissions can’t accurately distinguish whether an app is malicious or benign. 6070 benign apps and 9419 malware are used to evaluate the proposed approach. The SVOA and the number of the used permissions are learned and tested by machine learning. TF-IDF algorithm is used to calculate the permission value (PV) of each permission and the sensitivity value of apk (SVOA) of each app. The system permissions are extracted in Android application package’s (Apk’s) manifest file. Based on this cognition and perspective, in this paper, a new static detection method based on TF-IDF and Machine Learning is proposed. It can efficiently and accurately extract the permissions from an app. The static analysis method does not need to run the app. Term Frequency-Inverse Document Frequency (TF-IDF) is used to assess the importance of a word for a file set in a corpus. Permissions play a vital role in the security of the Android apps. Meanwhile, adopting more features will increase the complexity of the model and the computational cost of the system. However, due to improvements of malware itself and Android system, it is difficult to design a detection method that can efficiently and effectively detect malicious apps for a long time. The absence of third-party market regulation has prompted research institutions to propose different malware detection techniques. A large number of third-party Android application (app) markets have emerged. Android is the most widely used mobile operating system (OS).

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