JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (1): 42-47.doi: 10.6040/j.issn.1672-3961.1.2016.150

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Android malware detection based on SVM

ZHANG Yuling, YIN Chuanhuan*   

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
  • Received:2016-03-31 Online:2017-02-20 Published:2016-03-31

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Abstract: In order to detect malware effectively and reduce the threat of malicious software on Android platform security, two strategies that were probability statistics embedding and feature extraction were proposed based on the analysis of existing data sets.These strategies were used to transform high-dimensional data into low-dimensional data so as to reduce the dimension and the uncertainty of the extracted features. Support vector machine were used to classify these data. With these strategies, the time complexity of training process was reduced to 16.7 percent of the original time, and the ability of detecting unknown malware families was improved obviously. Moreover, these strategies were used with some popular classification algorithms, and the experimental results revealed that these strategies could achieve a better detection rate.

Key words: Android malware, SVM, probability statistics, feature extraction, dimensionality reduction

CLC Number: 

  • TP391
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