1
引 言
2
动机及威胁模型
图1 射频能量采集系统
图2 采集的YouTube(视频)、Facebook(社交)和WhatsApp(通信)的电压信号
图3 采集的视频类别三个App的电压信号
图4 AppListener 的窃听攻击示例
3
攻击框架
图5 AppListener概述
3.1 确定受害者数量
3.2 信号分离
图6 信号分离算法图示
4
评 估
图7 实验场景
图8 识别应用程序行为的准确度(%)
采样频率。更高的频率也会消耗更多的能量,在不同采样频率下的应用识别准确度以及相应的能耗之间需要取得良好的平衡。
5
讨 论
参考文献
[1] T. Ni, G. Lan, J. Wang, Q. Zhao, and W. Xu, “Eavesdrop ping mobile app activity via radio-frequency energy har vesting,” in 32nd USENIX Security Symposium (USENIX Security 23), pp. 3511–3528, 2023.
[2] B. Gao, Single channel blind source separation. PhD thesis, Newcastle University, 2011.
[3] D. Acharya, A. Rani, S. Agarwal, and V. Singh, “Application of adaptive savitzky–golay filter for eeg signal processing,” Perspectives in science, vol. 8, pp. 677–679, 2016.
[4] B. Saltaformaggio, H. Choi, K. Johnson, Y. Kwon, Q. Zhang, X. Zhang, D. Xu, and J. Qian, “Eavesdropping on {Fine-Grained} user activities within smartphone apps over encrypted network traffic,” in 10th USENIX Workshop on Offensive Technologies (WOOT 16), 2016.
[5] T. Van Ede, R. Bortolameotti, A. Continella, J. Ren, D. J. Dubois, M. Lindorfer, D. Choffnes, M. van Steen, and A. Peter, “Flowprint: Semi-supervised mobile-app fingerprinting on encrypted network traffic,” in Network and distributed system security symposium (NDSS), vol. 27, 2020.
[6] D. Li, W. Li, X. Wang, C.-T. Nguyen, and S. Lu, “Activetracker: Uncovering the trajectory of app activities over encrypted internet traffic streams,” in 2019 16th Annual IEEE international conference on sensing, communication, and networking (SECON), pp. 1–9, IEEE, 2019.
[7] J. Li, H. Zhou, S. Wu, X. Luo, T. Wang, X. Zhan, and X. Ma, “{FOAP}:{Fine-Grained}{Open-World} android app fingerprinting,” in 31st USENIX Security Symposium (USENIX Security 22), pp. 1579–1596, 2022.
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作者:高尚 中国科学院信息工程研究所
责编:高琪
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