Mining Based Learning Framework for Android Malware Detection

Authors(2) :-D.Sindhu, V. Bakyalakshmi

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.

Authors and Affiliations

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

Android, malware, Manifest files, data mining

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Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 450-454
Manuscript Number : CSEIT1726153
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

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.
Journal URL : http://ijsrcseit.com/CSEIT1726153

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