What a SHAME: Smart Assistant Voice Command Fingerprinting Utilizing Deep Learning
What a SHAME: Smart Assistant Voice Command Fingerprinting Utilizing Deep Learning
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DOI：
10.1145/3463676.3485615
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
John F. Hyland;Conrad Schneggenburger;N. Lim;Jake Ruud;Nate Mathews;M. Wright
中科院分区：
文献类型：
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作者：
John F. Hyland;Conrad Schneggenburger;N. Lim;Jake Ruud;Nate Mathews;M. Wright
据估计，到2024年，全球配备语音助理软件的系统总数将超过84亿台设备。虽然这些设备为消费者提供了便利，但它们存在无数的安全问题。本文重点介绍了智能助手加密网络流量元数据中的信息泄漏所暴露出的严重隐私威胁。为了调查这个问题，我们收集了一个新的数据集，该数据集由使用我们开发的数据收集和清理脚本向Amazon Echo Dot提出的动态和静态命令组成。此外，我们提出了智能家居助理恶意入侵模型（SHAME）作为新的最先进的语音命令指纹分类器。在对多个数据集进行评估时，我们的攻击正确地对加密的语音命令进行了分类，对Google Home流量的准确率高达99.81%，对Amazon Echo Dot流量的准确率高达95.2%。这些发现表明，必须采取安全措施来阻止互联网服务提供商、民族国家和网络窃听者监视我们的亲密对话。
It is estimated that by the year 2024, the total number of systems equipped with voice assistant software will exceed 8.4 billion devices globally. While these devices provide convenience to consumers, they suffer from a myriad of security issues. This paper highlights the serious privacy threats exposed by information leakage in a smart assistant's encrypted network traffic metadata. To investigate this issue, we have collected a new dataset composed of dynamic and static commands posed to an Amazon Echo Dot using data collection and cleaning scripts we developed. Furthermore, we propose the Smart Home Assistant Malicious Ensemble model (SHAME) as the new state-of-the-art Voice Command Fingerprinting classifier. When evaluated against several datasets, our attack correctly classifies encrypted voice commands with up to 99.81% accuracy on Google Home traffic and 95.2% accuracy on Amazon Echo Dot traffic. These findings show that security measures must be taken to stop internet service providers, nation-states, and network eavesdroppers from monitoring our intimate conversations.