Open Access
ARTICLE
Novel Android Malware Detection Method Based on Multi-dimensional Hybrid Features Extraction and Analysis
Yue Li1, Guangquan Xu2,3, Hequn Xian1,*, Longlong Rao3, Jiangang Shi4,*
1 College of Computer Science and Technology, Qingdao University, Shandong Province, 266071,China
2 Big Data School, Qingdao Huanghai University, Qingdao City, Shandong Province, 266427, China
3 Tianjin Key Laboratory of Advanced Networking (TANK), College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
4 Shanghai Shang Da Hai Run Information System Co., Ltd, Shanghai, 200444, China
* Corresponding Author: Jiangang Shi,
Intelligent Automation & Soft Computing 2019, 25(3), 637-647. https://doi.org/10.31209/2019.100000118
Abstract
In order to prevent the spread of Android malware and protect privacy
information from being compromised, this study proposes a novel multidimensional hybrid features extraction and analysis method for Android malware
detection. This method is based primarily on a multidimensional hybrid features
vector by extracting the information of permission requests, API calls, and
runtime behaviors. The innovation of this study is to extract greater amounts of
static and dynamic features information and combine them, that renders the
features vector for training completer and more comprehensive. In addition, the
feature selection algorithm is used to further optimize the extracted information
to remove a number of extraneous features, and a new multi-dimensional
hybrid features vector is obtained. The multi-dimensional hybrid features vector
is then used to train the classification model. Finally, the unknown samples are
detected and identified by using the obtained classification model. Our
experiment is conducted based on 359 malicious and 500 benign applications as
experimental samples, and the results indicate that our proposed method
performs better in the accuracy rate of Android malware detection compared
with those methods using static methods alone.
Keywords
Cite This Article
Y. Li, G. Xu, H. Xian, L. Rao and J. Shi, "Novel android malware detection method based on multi-dimensional hybrid features extraction and analysis,"
Intelligent Automation & Soft Computing, vol. 25, no.3, pp. 637–647, 2019.