In order to improve the performance of android security detection

this paper puts forward a secure detection model of android applications by jointly selecting features and classifier design. Firstly

android security detection features are extracted

and the feature and support vector machine parameter are combined to a Individual

and then teaching learning optimization algorithm is used to simulate teachers teaching process and students exchange process to find the best subset of features and support vector machine parameters

finally

the optimal android security detection model are established and the simulation experiment is carried. The experimental results show that

compared with other android security detection model

the proposed model has improved the accuracy rate of android security detection

and improved android security detection efficiency

so it can meet the real-time requirement of android security detection.