ISSN 
Android Malware Detection Based on Power Consumption Analysis
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摘要: 该文提出一种基于电量分析的恶意软件检测方法。首先获取移动终端的耗电状态并利用Mel频谱倒谱系数（MFCC）构建高斯混合模型（GMM）。然后采用GMM模型对电量消耗状态进行分析，进而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，表明基于软件的电量消耗信息分析可以较准确地检测出移动终端的恶意应用。Abstract: This paper proposes a malicious software detection method based on power consumption. Firstly, the mobile terminal's power consumption status is obtained, and the Gaussian mixture model (GMM) is built by using Mel frequency cepstral coefficients (MFCC). Then the GMM is used to analyze power consumption, and then identify malicious applications through the application software classification processing. Experiments show that an application software function and its power consumption have a close relationship, and some malicious applications in mobile terminals can be detected accurately through analyzing software power consumption information.
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表 1 特征提取算法的基本参数
参数 值 预加重 1-0.95z-1 采样率/kHz 8 量化/bit 8 加窗 海明窗 帧长 256 帧移 128 特征向量 32维MFCC GMM 12 
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表 2 软件检测结果
类别 结果 游戏 浏览器 音乐播放 恶意软件 游戏 78 9 3 10 浏览器 12 72 7 9 音乐播放 16 11 65 8 恶意软件 3 7 11 79
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期刊类型引用(1)
1. 蒋厚明，胡牧，曹海涛. 基于硬件运行时间的Android应用非线性能耗模型. 计算机与现代化. 2017(10): 116-120 . 
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