Table of Contents Author Guidelines Submit a Manuscript
Mobile Information Systems
Volume 2018, Article ID 1649703, 12 pages
Research Article

Detecting Android Malwares with High-Efficient Hybrid Analyzing Methods

1School of Electronic Information Engineering, Tianjin University, Tianjin 30072, China
2Teachers College of Columbia University, New York, NY 10027, USA

Correspondence should be addressed to Xiangdong Huang; nc.ude.ujt@gnauhdx

Received 30 July 2017; Revised 8 October 2017; Accepted 29 October 2017; Published 13 March 2018

Academic Editor: Xiapu Luo

Copyright © 2018 Yu Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In order to tackle the security issues caused by malwares of Android OS, we proposed a high-efficient hybrid-detecting scheme for Android malwares. Our scheme employed different analyzing methods (static and dynamic methods) to construct a flexible detecting scheme. In this paper, we proposed some detecting techniques such as Com+ feature based on traditional Permission and API call features to improve the performance of static detection. The collapsing issue of traditional function call graph-based malware detection was also avoided, as we adopted feature selection and clustering method to unify function call graph features of various dimensions into same dimension. In order to verify the performance of our scheme, we built an open-access malware dataset in our experiments. The experimental results showed that the suggested scheme achieved high malware-detecting accuracy, and the scheme could be used to establish Android malware-detecting cloud services, which can automatically adopt high-efficiency analyzing methods according to the properties of the Android applications.