Android Malware Detector Exploiting Convolutional Neural Network and Adaptive Classifier Selection

Convolutional Neural Network (CNN) has achieved success in Android malware detection and many other fields. However, the empirical evaluation of previous studies have shown that no single machine learning classifier is capable to provide the best accuracy in any context. In this paper, a new method for Android malware detection is proposed, we replace the single machine learning classifier in CNN with Adaptive Selection of Classifiers (ASC) to improve the performance of malware classification. We test our method on 1746 apk samples with 1000 malware, the result shows the accuracy of our approach performs 4.27% better than the state-of–art CNN model used in the current research.