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Malicious Application Detection and Classification System for Android Mobiles

Author

Listed:
  • Sapna Malik

    (Department of Computer Science Engineering, Maharaja Surajmal Institute of Technology, Delhi, India)

  • Kiran Khatter

    (School of Engineering and Technology, Ansal University, Gurgaon, India)

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.

Suggested Citation

  • Sapna Malik & Kiran Khatter, 2018. "Malicious Application Detection and Classification System for Android Mobiles," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 9(1), pages 95-114, January.
    • Handle: RePEc:igg:jaci00:v:9:y:2018:i:1:p:95-114
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