Exploring Feature Extraction and ELM in Malware Detection for Android Devices

Abstract

A huge increase in the number of mobile malware brings a serious threat to Internet security, as the adoption rate of mobile device is soaring, especially Android device. A variety of researches have been developed to defense malware, but the mobile device users continuously suffer private information leak or economic losses from malware. Recently, a large number of methods have been proposed based on static or dynamic features analysis combining with machine learning methods, which are considered effective to detect malware on mobile device. In this paper, we propose an effective framework to detect malware on Android device based on feature extraction and neural network calssifier. In this framework, we take use of static features to represent malware and utilize extreme learning machine (ELM) algorithm to learn the neural network. We first extract features from the malware, and then utilize three different feature extraction methods including principal component analysis (PCA), Karhunen-Loève transform (KLT) and independent component analysis (ICA) to transform the feature matrix into new feature spaces and generate three new feature matrixes. For each feature matrix, we construct En base classifiers by using ELM. Finally, we utilize Stacking method to combine the results. Experimental results suggest that the proposed framework is effective in detecting malware on Android device.