Android Malware Detection Method Based on Frequent Pattern and Weighted Naive Bayes

With the market share of Android system becoming the first in the world, the security problem of Android system is becoming more and more serious. How to effectively detect Android malware has become a significant problem. Permissions and API calls in Android applications can effectively reflect the behavior patterns of an Android application. Most researchers have only considered a single permission or API feature, and did not consider associations and patterns inside the permission or API features. Some scholars have also tried to find the combination modes inside the permission features in malwares, but the detection of maliciousness according to this combination mode is too absolute. This paper proposes a malware detection method, which combines the advantages of frequent pattern mining and Naive Bayes to effectively identify Android malwares.