Mobile Malware Detection Based on Optimized Fuzzy C-Means

doi:10.3969/j.issn.1671-1122.2016.01.009

Abstract

Abstract:

In order to improve the effectiveness of mobile malware detection, the optimized euzzy C-means (FCM) clustering algorithm is used to classify and detect massive amounts of malware automatically. Firstly, this paper presents a new algorithm named of intelligent bat algorithm (IBA) by introducing a gravitation operator to enhance the linkage of the Bat algorithm , and uses it to optimize the FCM. After the optimization, the FCM can significantly improve the detection efficiency of mobile malware. The simulation experiments show that the IBA has a better global search capability and optimization precision, and the FCM optimized by IBA has higher stability and better clustering accuracy , and the effect is good for mobile malware detection.

Key words: malware detection, intelligent bat algorithm (IBA), fuzzy C-means (FCM)

CLC Number:

TP309

Shifeng HUANG, Yajun GUO, Jianqun CUI, Qingjiang ZENG. Mobile Malware Detection Based on Optimized Fuzzy C-Means[J]. Netinfo Security, 2016, 16(1): 45-50.

Figures/Tables 4

References 25

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