A Detecting Method for Malicious Mobile Application Based on Incremental SVM

摘要

Due to the rapid growth of android malicious application samples, traditional detection methods need to spend a lot of time for training, a detecting method for malicious mobile application based on incremental SVM was proposed to achieve incremental learning of the detection system. The method used the SVM as the classification and training algorithm, and extracted sensitive permissions and APIs as application characteristics. On the basis of SVM, a dual weight function was designed to filter the historical training samples to avoid redundant samples, and the incremental learning method of SVM was implemented in combination with KKT conditions. Therefore, the training time could be reduced and the learning efficiency of the malicious application detection system could be improved without reducing the training accuracy.