Mobile Malware Classification

  • Authors

    • Zolidah Kasiran
    • Norkhushaini Awang
    • Fatin Nurhanani Rusli
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.31.23369
  • Classification, K-means, Malware, Mobile Malware

  • Abstract

    Android malware is growing in such an exponential pace which lead to the need of an efficient malware intrusion  detection technique. The single approach of clustering or classification technique in malware intrusion detection yield to high negative positive alarm rate.. This project had proposed clustering in intrusion detection method using hybrid learning approaches combining K-Means clustering and Naïve Bayes classification had been proposed.  The result had shown the improved false rate alarm in malware detection.

     

     

  • References

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  • How to Cite

    Kasiran, Z., Awang, N., & Nurhanani Rusli, F. (2018). Mobile Malware Classification. International Journal of Engineering & Technology, 7(4.31), 209-2013. https://doi.org/10.14419/ijet.v7i4.31.23369

    Received date: 2018-12-07

    Accepted date: 2018-12-07

    Published date: 2018-12-09