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Author:

Zhao, Xiaoyan (Zhao, Xiaoyan.) | Fang, Juan (Fang, Juan.) (Scholars:方娟) | Wang, Xiujuan (Wang, Xiujuan.)

Indexed by:

EI Scopus

Abstract:

In this paper, we propose a permission-based malware detection framework for Android platform. The proposed framework uses PCA(Principal Component Analysis) algorithm for features selection after permissions extracted, and applies SVM(support vector machine) methods to classify the collected data as benign or malicious in the process of detection. The simulation experimental results suggest that this proposed detection framework is effective in detecting unknown malware, and compared with traditional antivirus software, it can detect unknown malware effectively and immediately without updating the newest malware sample library in time. It also illustrates that using permissions features alone with machine learning methods can achieve good detection result.

Keyword:

Computer viruses Principal component analysis Feature extraction Android (operating system) Support vector machines

Author Community:

  • [ 1 ] [Zhao, Xiaoyan]College of Computer Science, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Fang, Juan]College of Computer Science, Beijing University of Technology, Beijing, 100124, China
  • [ 3 ] [Wang, Xiujuan]College of Computer Science, Beijing University of Technology, Beijing, 100124, China

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Source :

Year: 2014

Issue: 650 CP

Volume: 2014

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 19

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