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DecaDroid Classification and Characterization of Malicious Behaviour in Android Applications

DecaDroid Classification and Characterization of Malicious Behaviour in Android Applications

Charu Gupta, Rakesh Kumar Singh, Simran Kaur Bhatia, Amar Kumar Mohapatra
Copyright: © 2020 |Volume: 14 |Issue: 4 |Pages: 17
ISSN: 1930-1650|EISSN: 1930-1669|EISBN13: 9781799805380|DOI: 10.4018/IJISP.2020100104
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MLA

Gupta, Charu, et al. "DecaDroid Classification and Characterization of Malicious Behaviour in Android Applications." IJISP vol.14, no.4 2020: pp.57-73. http://doi.org/10.4018/IJISP.2020100104

APA

Gupta, C., Singh, R. K., Bhatia, S. K., & Mohapatra, A. K. (2020). DecaDroid Classification and Characterization of Malicious Behaviour in Android Applications. International Journal of Information Security and Privacy (IJISP), 14(4), 57-73. http://doi.org/10.4018/IJISP.2020100104

Chicago

Gupta, Charu, et al. "DecaDroid Classification and Characterization of Malicious Behaviour in Android Applications," International Journal of Information Security and Privacy (IJISP) 14, no.4: 57-73. http://doi.org/10.4018/IJISP.2020100104

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Abstract

Widespread use of Android-based applications on the smartphones has resulted in significant growth of security attack incidents. Malware-based attacks are the most common attacks on Android-based smartphones. To forestall malware from attacking the users, a much better understanding of Android malware and its behaviour is required. In this article, an approach to classify and characterise the malicious behaviour of Android applications using static features, data flow analysis, and machine learning techniques has been proposed. Static features like hardware components, permissions, Android components and inter-component communication along with unique source-sink pairs obtained from data flow analysis have been used to extract the features of the Android applications. Based on the features extracted, the malicious behaviour of the applications has been classified to their respective malware family. The proposed approach has given 95.19% accuracy rate and F1 measure of 92.19302 with the largest number of malware families classified as compared to previous work.

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