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A Fast Approach Towards Android Malware Detection

  • Hongmei ChiEmail author
  • Xavier Simms
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9155)

Abstract

The proposed research compares the feasibility of three well known machine learning algorithms on the detection of malware on the Android platform. Once accuracy is at an acceptable level, these algorithms performance are further enhanced to decrease analysis time, which can lead to faster detection rates. The framework makes use of powerful GPU’s (Graphics Processing Unit) in order to reduce the time spent on computation for malware detection. Utilizing MATLAB’s parallel computing kit, we can execute analysis at a much higher speed due to the increased cores in the GPU. A reduced computation time allows for quick updates to the user about zero day malware, resulting in a decreased impact. With the increase in mobile devices unending, quick detection will become necessary to combat mobile malware, and with Android alone reaching its 50 billionth app downloads will be no small task.

Keywords

Malware Vulnerability detection Andriod GPU Data mining 

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References

  1. 1.
    Canalys, Mobile device market to reach 2.6 billion units by 2016, Press release 2013/056, February 22, 2013. http://www.canalys.com/newsroom/mobile-device-market-reach-26-billion-units-2016
  2. 2.
    Aaron, S.: Pew Internet Project. Pew Research center (June 2012)Google Scholar
  3. 3.
    Carolina, M., Lillian, T., Roberta, C., Ranjit, A., Hut, N.T., Tracy, T., Annette, Z.: Forecast: Devices by Operating System and User Type, Worldwide, 2010–2017, 2Q13 Update, Garter ResearchGoogle Scholar
  4. 4.
    Zhou, Y., Wang, Z., Zhou, W., Jiang, X.: Hey, you, get off of my market: detecting malicious apps in official and alternative android markets. In: Proceedings of the 19th Annual Network and Distributed System Security Symposium (February 2012)Google Scholar
  5. 5.
    Juniper Networks Mobile Threat Center: Third annual mobile threats report: March 2012 through March 2013 (March 2013). http://www.juniper.net/us/en/local/pdf/additional-resources/3rd-jnpr-mobile-threats-report-exec-summary.pdf
  6. 6.
    Vanja, S.: SophosLabs, When Malware Goes Mobile: Causes, Outcomes and Cures. http://www.sophos.com/enus/medialibrary/Gated%20Assets/white%20papers/Sophos_Malware_Goes_Mobile.pdf
  7. 7.
    Burguera, I., Zurutuza, U., Nadjm-Tehrani, S.: Crowdroid: behavior-based malware detection system for android. In: Proceedings of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices. ACM (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer and Information SciencesFlorida A&M UniversityTallahasseeUSA

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