Advertisement

Android Malware Detection Techniques

  • Shreya KhemaniEmail author
  • Darshil Jain
  • Gaurav Prasad
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 906)

Abstract

Importance of personal data has increased along with the evolution of technology. To steal and misuse this data, malicious programs and software are written to exploit the vulnerabilities of the current system. These programs are referred to as malware. Malware harasses the users until their intentions are fulfilled. Earlier malware was major threats to the personal computers. However, now there is a lateral shift in interest toward Android operating system, which has a large market share in smartphones. Day by day, malware is getting stronger and new type of malware is being written so that they are undetected by the present software. Security parameters must be changed to cope up with the changes happening around the world. In this paper, we discuss the different types of malware analysis techniques which are proposed till date to detect the malware in Android platform. Moreover, it also analyzes and concludes about the suitable techniques applicable to the different type of malware.

Keywords

Android malware Static analysis Hybrid analysis Detection techniques Dynamic analysis 

References

  1. 1.
    Digital in 2017: Global overview. March 2018, URL: https://wearesocial.com/special-reports/digital-in-2017-globaloverview.
  2. 2.
    Android-statistics and facts. March 2018. URL: https://www.statista.com/topics/876/android/.
  3. 3.
    Mobile malware evolution 2016. Retrieved March 2018 from https://securelist.com/mobile-malware-evolution-2016/77681/.
  4. 4.
    McAfee labs threat report December 2017. Retrieved March 2018 from https://www.mcafee.com/us/resource/reports/rp-quartely-threats-dec-2017.pdf.
  5. 5.
    Seth, R., Kaushal, R. (2015). Permission based malware analysis and detection in android.Google Scholar
  6. 6.
    Liu, X., Liu, J. (2014, April). A two-layered Permission-based android malware detection scheme. In Proceedings of 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, Oxford, UK (pp. 142–148).Google Scholar
  7. 7.
    Shahriar, H., Islam, M., Clincy, V. (2017). Android malware detection using permission analysis. In Proceedings of Southeast Con, Charlotte, NC, USA.Google Scholar
  8. 8.
    Faruki, P., Laxmi, V., Bharmal, A., Gaur, M. S., & Ganmoor, V. (2015). AndroSimilar: Robust signature for detecting variants of android malware. Journal of Information Security and Applications, 22, 66–80.CrossRefGoogle Scholar
  9. 9.
    Egele, M., Scholte, T., Kirda, E., & Kruegel, C. (2012). A survey on automated dynamic analysis tools and techniques. ACM Computing Surveys, 44(2), 1–42.CrossRefGoogle Scholar
  10. 10.
    Burguera, I., Zurutuza, U., & Nadjm-Tehrani, S. (2011). Crowdroid: Behaviour-based malware detection system for android. In Proceedings of the 1st ACM Work. Security and Privacy in Smartphones and Mobile Devices-SPSM’11 (p. 15).Google Scholar
  11. 11.
    Portokalidis, G., Homburg, P., Anagnostakis, K., Bos, H. (2010).Paranoid android: Versatile protection For smartphones. In Proceedings of the 26th Annual Computer Security Applications Conference, ASCAC’10 (pp. 347–356).Google Scholar
  12. 12.
    Shabtai, A., Kanonov, U., Elovici, Y., Glezer, C., & Weiss, Y. (2011). Andromaly: A behavioral malware detection framework for android devices. Journal of Intelligent Information Systems, 1–30.Google Scholar
  13. 13.
    Enck, W., Gilbert, P., Chun, B. G., Jung, J., McDaniel, P., & Sheth, A. N. (2010). Taintdroid: An information flow tracking system for real-time privacy monitoring on smartphones. In Osdi’10 (Vol. 49, pp. 1–6).Google Scholar
  14. 14.
    Amos, B., Turner, H., & White, J. (2013). Applying machine learning classifiers to dynamic android malware detection at scale (pp. 1666–1671).Google Scholar
  15. 15.
    McLaughlin, N., Del Rincon, J. M., Kang, B., Yerima, S., Miller, P., Sezer, S. … Ahn, G. J. (2017). Deep android malware detection. In Proceedings of CODASPY, Scottsdale, Arizona, USA.Google Scholar
  16. 16.
    Demontis, A., Melis, M., Biggio, B., Maiorca, D., Arp, D., Rieck, K. … Roli, F. (2017). Yes, Machine learning can be more secure! A case study on android malware D=detection. IEEE Transactions on Dependable and Secure Computing.Google Scholar
  17. 17.
    Idrees, F., Rajarajan, M., Conti, M., Chen, T. M., & Rahulamathavan, Y. (2017). PIndroid: A novel android malware detection system using ensemble learning methods. Computer and Security, 68, 36–46.CrossRefGoogle Scholar
  18. 18.
    Wei, F., Li, Y., Roy, S., Ou, X., & Zhou, W. (2017). Deep ground truth analysis of current android malware. In International Conference on Detection of Intrusions and Malware, and Vulnerability and Assessment (DIMVA) (pp. 252–276).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of CCEManipal University JaipurJaipurIndia

Personalised recommendations