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.

References

  1. Christy Pettey and Holly Stevens. Gartner says 428 million mobile communication devices sold worldwide in first quarter 2011, a 19 percent
  2. increase year-on-year. http://www.gartner.com/it/page.jsp?id=1689814.
  3. Christy Pettey and Holly Stevens. Gartner says sales of mobile devices grew 5.6 percent in third quarter of 2011; smartphone sales increased 42 percent. http://www.gartner.com/it/page.jsp?id=1848514.
  4. Frank, M., Dong, B., Felt, A. P., & Song, D. (2012, December). Mining permission request patterns from android and face book applications. In Data Mining (ICDM), 2012 IEEE 12th International Conference on (pp. 870-875). IEEE.
  5. Barrera, D., Kayacik, H. G., van Oorschot, P. C., & Somayaji, A. (2010, October). A methodology for empirical analysis of permission-based security models and its application to android. In Proceedings of the 17th ACM conference on Computer and communications security (pp. 73-84). ACM.
  6. Sanz, B., Santos, I., Laorden, C., Ugarte-Pedrero, X., Bringas, P. G., & Álvarez, G. (2013). Puma: Permission usage to detect malware in android. In International Joint Conference CISIS’12-ICEUTE´ 12-SOCO´ 12 Special Sessions (pp. 289-298). Springer Berlin Heidelberg.
  7. Rassameeroj, I., & Tanahashi, Y. (2011, May). Various approaches in analyzing android applications with its permission-based security models. In Electro/Information Technology (EIT), 2011 IEEE International Conference on (pp. 1-6). IEEE.
  8. Peiravian, N., & Zhu, X. (2013, November). Machine learning for android malware detection using permission and api calls. In Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on (pp. 300-305). IEEE.
  9. Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K., & Siemens, C. E. R. T. (2014, February). DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket. In NDSS.
  10. Aafer, Y., Du, W., & Yin, H. (2013, September). Droidapiminer: Mining api-level features for robust malware detection in android. In International Conference on Security and Privacy in Communication Systems (pp. 86-103). Springer International Publishing.
  12. Dini, G., Martinelli, F., Saracino, A., & Sgandurra, D. (2012, October). MADAM: a multi-level anomaly detector for android malware. In International Conference on Mathematical Methods, Models, and Architectures for Computer Network Security (pp. 240-253). Springer Berlin Heidelberg.
  13. Sahs, J., & Khan, L. (2012, August). A machine learning approach to android malware detection. In Intelligence and security informatics conference (eisic), 2012 european (pp. 141-147). IEEE.
  14. Shabtai, A., Kanonov, U., Elovici, Y., Glezer, C., & Weiss, Y. (2012). “Andromaly”: a behavioral malware detection framework for android devices. Journal of Intelligent Information Systems, 38(1), 161-190.
  15. Yan, G., Brown, N., & Kong, D. (2013, July). Exploring discriminatory features for automated malware classification. In International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (pp. 41-61). Springer, Berlin, Heidelberg.
  16. Lindorfer, M., Neugschwandtner, M., Weichselbaum, L., Fratantonio, Y., Van Der Veen, V., & Platzer, C. (2014, September). Andrubis--1,000,000 apps later: A view on current Android malware behaviors. In Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS), 2014 Third International Workshop on (pp. 3-17). IEEE.
  17. Huang, C. Y., Tsai, Y. T., & Hsu, C. H. (2013). Performance evaluation on permission-based detection for android malware. In Advances in Intelligent Systems and Applications-Volume 2 (pp. 111-120). Springer Berlin Heidelberg.

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, IInternational 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.