Mobile Malware Classification - Kasiran, Zolidah; Awang, Norkhushaini; Nurhanani Rusli, Fatin (International Journal of Engineering and Technology, 2018)
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.