Android malware detection based on the system power consumption

doi:10.16511/j.cnki.qhdxxb.2017.21.009

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Abstract The power consumption sequential waveform of an Android application while running is similar to the acoustic signal. This paper presents a malware detection algorithm based on the Mel frequency cepstral coefficients (MFCC). The algorithm calculates the MFCC of the power consumption sequential waveform and constructs a Gaussian mixture model (GMM) from the MFCC distribution. Then, the GMM is used to analyze power consumption to identify malicious software through the application classification process. Tests show that the application software functionality and power consumption are closely related and that the software-based power consumption information analysis can accurately detect mobile terminal malware.

Keywords mobile terminal power consumption Mel frequency cepstral coefficients Gaussian mixture model

ZTFLH:

TP309.1

Issue Date: 15 January 2017

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	YANG Hongyu
	TANG Ruiwen

Cite this article:

YANG Hongyu,TANG Ruiwen. Android malware detection based on the system power consumption[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(1): 44-49.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2017.21.009 OR http://jst.tsinghuajournals.com/EN/Y2017/V57/I1/44

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