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)


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.


Malware Vulnerability detection Andriod GPU Data mining 


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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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