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.
杨宏宇, 唐瑞文. 基于电量消耗的Android平台恶意软件检测[J]. 清华大学学报（自然科学版）, 2017, 57(1): 44-49.
YANG Hongyu, TANG Ruiwen. Android malware detection based on the system power consumption. Journal of Tsinghua University(Science and Technology), 2017, 57(1): 44-49.
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2017, Vol. 57 
