Mining Based Learning Framework for Android Malware Detection

Authors

  • D.Sindhu  M.Phil. Research Scholar, Department of Computer Applications, Sri Jayendra Saraswathy Maha Vidyalaya College of Arts & Science, , Coimbatore, Tamil Nadu, India
  • V. Bakyalakshmi  Associate Professor,Department of Computer Applications, Sri Jayendra Saraswathy Maha Vidyalaya College of Arts & Science, , Coimbatore, Tamil Nadu, India

Keywords:

Android, malware, Manifest files, data mining

Abstract

The Android malware threat has increased owing to the increase popularity of Android smartphones. The widespread adoption and contextually sensitive nature of smartphone devices has increased concerns over Android malware writers. Mining based learning framework is proposed for detecting malicious applications on Android devices. The system begins with analyzes only manifest files that are required to classify the Android applications into malware or benign applications. It realizes a lightweight approach for detection, and its effectiveness is experimentally confirmed by employing real samples of Android malware. The result shows that the new method can effectively detect Android malware, even when the sample is unknown.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-12-31

Issue

Volume 2, Issue 6, November-December-2017

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
D.Sindhu, V. Bakyalakshmi, " Mining Based Learning Framework for Android Malware Detection" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.450-454, November-December-2017.