@Article{2019.100000118,
AUTHOR = {Yue Li, Guangquan Xu, Hequn Xian, Longlong Rao, Jiangang Shi},
TITLE = {Novel Android Malware Detection Method Based on Multi-dimensional  Hybrid Features Extraction and Analysis},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {3},
PAGES = {637--647},
URL = {http://www.techscience.com/iasc/v25n3/39692},
ISSN = {2326-005X},
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.},
DOI = {10.31209/2019.100000118}
}