Random Forest Classification for Detecting Android Malware

  title={Random Forest Classification for Detecting Android Malware},
  author={Mohammed S. Alam and Son Thanh Vuong},
  journal={2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing},
Internet connected smartphone devices play a crucial role in the application domain of Internet of Things. These devices are being widely used for day-to-day activities such as remotely controlling lighting and heating at homes, paying for parking, and recently for paying for goods using saved credit card information using Near Field Communication (NFC). Android is the most popular smartphone platform today. It is also the choice of malware authors to obtain secure and private data. In this… CONTINUE READING
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Key Quantitative Results

  • Our experimental results based on 5-fold cross validation of our dataset shows that Random Forest performs very well with an accuracy of over 99 percent in general, an optimal Out-Of-Bag (OOB) error rate [3] of 0.0002 for forests with 40 trees or more, and a root mean squared error of 0.0171 for 160 trees.


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Publications referenced by this paper.
Showing 1-10 of 15 references

Antimalware”. Internet: https://github.com/VT-Magnum- Research/antimalware

B. Amos
[May 15, • 2013
View 11 Excerpts
Highly Influenced

Random Forests

Machine Learning • 2001
View 5 Excerpts
Highly Influenced

Analyzing Android Network Traffic

M. Cerdano
Internet: http://mobile.tutsplus.com/tutorials/android/analyzing-androidnetwork-traffic/, [May • 2013
View 1 Excerpt

Internet: http://contagiominidump.blogspot.ca/, [May

M. Parkour
View 1 Excerpt

Random Forests”. Internet: http://www.stat.berkeley.edu/ breiman/RandomForests/cc home.htm, [May

L. Breiman, A. Cutler
View 1 Excerpt

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