基于电量消耗的Android平台恶意软件检测

杨宏宇, 唐瑞文

清华大学学报(自然科学版) ›› 2017, Vol. 57 ›› Issue (1) : 44-49.

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清华大学学报(自然科学版) ›› 2017, Vol. 57 ›› Issue (1) : 44-49. DOI: 10.16511/j.cnki.qhdxxb.2017.21.009
计算机科学与技术

基于电量消耗的Android平台恶意软件检测

  • 杨宏宇, 唐瑞文
作者信息 +

Android malware detection based on the system power consumption

  • YANG Hongyu, TANG Ruiwen
Author information +
文章历史 +

摘要

根据Android应用在运行期的耗电时序波形与声波信号类似的特点,该文提出了一种基于Mel频谱倒谱系数(Mel frequency cepstral coefficients,MFCC)的恶意软件检测算法。首先计算耗电时序波形的MFCC,根据MFCC的分布构建Gauss混合模型(Gaussian mixture model,GMM)。然后采用GMM对电量消耗进行分析,通过对应用软件的分类处理识别恶意软件。实验证明:应用软件的功能与电量消耗关系密切,并且基于软件的电量消耗信息分析可以较准确地对移动终端的恶意软件进行检测。

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.

关键词

移动终端 / 电量消耗 / Mel频谱倒谱系数 / Gauss混合模型

Key words

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

引用本文

导出引用
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 https://doi.org/10.16511/j.cnki.qhdxxb.2017.21.009
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 https://doi.org/10.16511/j.cnki.qhdxxb.2017.21.009
中图分类号: TP309.1   

参考文献

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