Preliminary Results of Applying Machine Learning Algorithms to Android Malware Detection

@article{Leeds2016PreliminaryRO,
  title={Preliminary Results of Applying Machine Learning Algorithms to Android Malware Detection},
  author={Matthew Leeds and Travis Atkison},
  journal={2016 International Conference on Computational Science and Computational Intelligence (CSCI)},
  year={2016},
  pages={1070-1073}
}
  • M. Leeds, T. Atkison
  • Published 1 December 2016
  • Computer Science
  • 2016 International Conference on Computational Science and Computational Intelligence (CSCI)
As the use of mobile devices continues to increase, so does the need for sophisticated malware detection algorithms. The preliminary research presented in this paper focuses on examining permission requests made by Android apps as a means for detecting malware. By using a machine learning algorithm, we are able to differentiate between benign and malicious apps. The model presented achieved a classification accuracy between 75% and 80% for our dataset and the best combination of parameters… 

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  • DOI 10.1109/CSCI.2016.203 1066 2016 International Conference on Computational Science and Computational Intelligence203 1066 2016 International Conference on Computational Science and Computational Intelligence IEEE DOI 10.1109/CSCI.2016.203 1070 2016 International Conference on Computational Scienc
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