Autosoft Journal

Online Manuscript Access


Novel Android Malware Detection Method Based on Multi-dimensional Hybrid Features Extraction and Analysis


Authors



Abstract

In order to prevent the spread of Android malware and protect privacy information from being compromised, this study proposes a novel multi-dimensional 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


Pages

Total Pages: 10

DOI
10.31209/2019.100000118


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways


Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Published

Online Article

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




CONTACT INFORMATION


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048
EMAIL: tsiepress@gmail.com
WEB: http://www.wacong.org/tsi/