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Malware Detection in Android Apps Using Static Analysis

Malware Detection in Android Apps Using Static Analysis

Nishtha Paul, Arpita Jadhav Bhatt, Sakeena Rizvi, Shubhangi
Copyright: © 2022 |Volume: 24 |Issue: 3 |Pages: 25
ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781799878223|DOI: 10.4018/JCIT.20220701.oa6
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MLA

Paul, Nishtha, et al. "Malware Detection in Android Apps Using Static Analysis." JCIT vol.24, no.3 2022: pp.1-25. http://doi.org/10.4018/JCIT.20220701.oa6

APA

Paul, N., Bhatt, A. J., Rizvi, S., & Shubhangi. (2022). Malware Detection in Android Apps Using Static Analysis. Journal of Cases on Information Technology (JCIT), 24(3), 1-25. http://doi.org/10.4018/JCIT.20220701.oa6

Chicago

Paul, Nishtha, et al. "Malware Detection in Android Apps Using Static Analysis," Journal of Cases on Information Technology (JCIT) 24, no.3: 1-25. http://doi.org/10.4018/JCIT.20220701.oa6

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Abstract

Frequency of malware attacks because Android apps are increasing day by day. Current studies have revealed startling facts about data harvesting incidents, where user’s personal data is at stake. To preserve privacy of users, a permission induced risk interface MalApp to identify privacy violations rising from granting permissions during app installation is proposed. It comprises of multi-fold process that performs static analysis based on app’s category. First, concept of reverse engineering is applied to extract app permissions to construct a Boolean-valued permission matrix. Second, ranking of permissions is done to identify the risky permissions across category. Third, machine learning and ensembling techniques have been incorporated to test the efficacy of the proposed approach on a data set of 404 benign and 409 malicious apps. The empirical studies have identified that our proposed algorithm gives a best case malware detection rate of 98.33%. The highlight of interface is that any app can be classified as benign or malicious even before running it using static analysis.