关键词: Android恶意应用, 集成学习, 投票算法

Abstract:

Aiming at the detection technology of malicious application on Android platform, an Android malicious program detection method MASV（Soft-Voting Algorithm） based on ensemble learning voting algorithm is proposed to effectively classify unknown applications. The underlying data for the experiment are obtained from a known open source dataset by using an application set of 213 256 benign applications and 18 363 malicious applications. First, the dimensionality of features is reduced by using the SVM-RFE feature selection algorithm. Then, a collection of multiple classifiers is used to detect malicious applications and benign applications, the classifiers include SVM（Support Vector Machine）, [K]-NN [（K]-Nearest Neighbor）, NB（Na?ve Bayes）, CART（Classification and Regression Tree） and RF（Random Forest）. At the same time, the gradient ascent algorithm is used to determine the base classifier weight parameter of the ensemble learning soft vote. The experimental results show that the method achieves an accuracy of 99.27% in malicious application detection.

Key words: Android malicious application, ensemble learning, voting algorithm