Welcome to the InfoSci Platform
Reference Hub5
Community Based Feature Selection Method for Detection of Android Malware

Community Based Feature Selection Method for Detection of Android Malware

Abhishek Bhattacharya, Radha Tamal Goswami
Copyright: © 2018 |Volume: 26 |Issue: 3 |Pages: 24
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781522542186|DOI: 10.4018/JGIM.2018070105
Cite Article Cite Article

MLA

Bhattacharya, Abhishek, and Radha Tamal Goswami. "Community Based Feature Selection Method for Detection of Android Malware." JGIM vol.26, no.3 2018: pp.54-77. http://doi.org/10.4018/JGIM.2018070105

APA

Bhattacharya, A. & Goswami, R. T. (2018). Community Based Feature Selection Method for Detection of Android Malware. Journal of Global Information Management (JGIM), 26(3), 54-77. http://doi.org/10.4018/JGIM.2018070105

Chicago

Bhattacharya, Abhishek, and Radha Tamal Goswami. "Community Based Feature Selection Method for Detection of Android Malware," Journal of Global Information Management (JGIM) 26, no.3: 54-77. http://doi.org/10.4018/JGIM.2018070105

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The amount of malware has been rising drastically as the Android operating system enabled smartphones and tablets are gaining popularity around the world in last couple of years. One of the popular methods of static detection techniques is permission/feature-based detection of malware through the AndroidManifest.xml file using machine learning classifiers. Ignoring important features or keeping irrelevant features may specifically cause mystification to classification algorithms. Therefore, to reduce classification time and improve accuracy, different feature reduction tools have been used in past literature. Community detection is one of the major tools in social network analysis but its implementation in the context of malware detection is quite rare. In this article, the authors introduce a community-based feature reduction technique for Android malware detection. The proposed method is evaluated on two datasets consisting of 3004 benign components and 1363 malware components. The proposed community-based feature reduction technique produces a classification accuracy of 98.20% and ROC value up to 0.989.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.