A Survey on The Novel Approach to Detect Malware Variants by User Oriented Behavior Based System for Android

Authors

  • A. Arulmurugan  Associate Professor, Department of Information Technology, A.V.C College of Engineering, Tamilnadu, India
  • B. Poonguzhali  UG Students, Department of Information Technology, A.V.C College of Engineering, Tamilnadu, India
  • M. Sugammathi  UG Students, Department of Information Technology, A.V.C College of Engineering, Tamilnadu, India
  • M. Madhumathi  UG Students, Department of Information Technology, A.V.C College of Engineering, Tamilnadu, India

Keywords:

Malware, big data security, iterative systems, feature selection.

Abstract

Android Malware is critical challenges for security of big data. Android users are capable to malicious application that can hack into their personal data in device due to the lack of careful monitoring of their device security. We categorize many of the most recent antimalware techniques based on their detection methods. It includes water marking stepping stone mechanisms. It provides the Android security solution. The paper propose framework for Android malware detection have been obtained by the ICFS procedure.

References

  1. A. Chonka, and J. Abawajy Detecting and mitigating HX-DoS attacks against cloud web services, IEEE 15th    International Conference on Network-Based Information Systems (NBiS), Pages 429-434, 2012.
  2. Charlie Miller, "Mobile Attacks and Defence", IEEE computer and reliability society on July 2011.
  3. C.J. DOrazioa, and K-K. R. Choo,"Circumventing iOS security mechanisms for APT forensicinvestigations: A   security taxonomy for cloud apps," Future Generation Computer Systems, Available online 17 November 2016.
  4. Dafang Zhang, "Fest: A Feature Extraction and Selection Tool for Android Malware Detection", in 20th IEEE    Symposium on Computers and Communication, ISCC 2015, 2015, pp.714-720
  5. Q. Do, B. Martini, K-K. R. Choo,"A Data Exfiltration and Remote Exploitation Attack on Consumer 3D Printers", Information Forensics and Security IEEE Transactions on, vol. 11, pp. 2174-2186, 2016, ISSN 1556-6013.
  6. Muneer Ahmad Dar,"Evaluating Smartphone Application Security: A Case Study on Android", Global Journal of Computer Science and Technology Network, Web & Security Volume 13 Issue 12 Version 1.0 Year 2013.
  7. Sajid Nabi Khan, "Review on Android App Security", International Journal of Advanced Research in  Computer Science and Software Engineering,   Volume 7, Issue 4, April 2017.
  8. Sumedh.P, "security in android based smartphone", International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 3, Issue 3, March 2014.
  9. Tiwari Mohini, "Review on Android and Smartphone Security", Research Journal of Computer and Information Technology Sciences_ISSN 2320 – 6527 Vol. 1(6), 12-19, November (2013).
  10. Y. Zhou, Z. Wang, W. Zhou, and X. Jiang, "Hey, you, get off of my market: Detecting malicious apps in official and alternative android markets." in NDSS, 2012

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

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Published

2018-02-28

Issue

Volume 3, Issue 1, January-February-2018

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]
A. Arulmurugan, B. Poonguzhali, M. Sugammathi, M. Madhumathi, " A Survey on The Novel Approach to Detect Malware Variants by User Oriented Behavior Based System for Android , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.1319-1324, January-February-2018.