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Malicious Application Detection and Classification System for Android Mobiles

Malicious Application Detection and Classification System for Android Mobiles

Sapna Malik, Kiran Khatter
Copyright: © 2018 |Volume: 9 |Issue: 1 |Pages: 20
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781522543527|DOI: 10.4018/IJACI.2018010106
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MLA

Malik, Sapna, and Kiran Khatter. "Malicious Application Detection and Classification System for Android Mobiles." IJACI vol.9, no.1 2018: pp.95-114. http://doi.org/10.4018/IJACI.2018010106

APA

Malik, S. & Khatter, K. (2018). Malicious Application Detection and Classification System for Android Mobiles. International Journal of Ambient Computing and Intelligence (IJACI), 9(1), 95-114. http://doi.org/10.4018/IJACI.2018010106

Chicago

Malik, Sapna, and Kiran Khatter. "Malicious Application Detection and Classification System for Android Mobiles," International Journal of Ambient Computing and Intelligence (IJACI) 9, no.1: 95-114. http://doi.org/10.4018/IJACI.2018010106

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Abstract

The Android Mobiles constitute a large portion of mobile market which also attracts the malware developer for malicious gains. Every year hundreds of malwares are detected in the Android market. Unofficial and Official Android market such as Google Play Store are infested with fake and malicious apps which is a warning alarm for naive user. Guided by this insight, this paper presents the malicious application detection and classification system using machine learning techniques by extracting and analyzing the Android Permission Feature of the Android applications. For the feature extraction, the authors of this work have developed the AndroData tool written in shell script and analyzed the extracted features of 1060 Android applications with machine learning algorithms. They have achieved the malicious application detection and classification accuracy of 98.2% and 87.3%, respectively with machine learning techniques.

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