Android malware detection method based on highly distinguishable static features and DenseNet.

ABSTRACT: The rapid growth of malware has become a serious problem that threatens the security of the mobile ecosystem and needs to be studied and resolved. Android is the main target of attackers due to its open source and popularity. To solve this serious problem, an accurate and efficient malware detection method is needed. Most existing methods use a single type of feature, which can be easily bypassed, resulting in low detection accuracy. In addition, although multiple types of features are used in some methods to solve the drawbacks of detection methods using a single type of feature, there are still some problems. Firstly, due to multiple types of features, the number of features in the initial feature set is extremely large, and some methods directly use them for training, resulting in exces

SUBMITTER: Yang J 

PROVIDER: S-EPMC9683612 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature