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Amazon Echo Security: Machine Learning to Classify Encrypted Traffic

机译:Amazon Echo Security:机器学习分类加密流量

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As smart speakers like the Amazon Echo become more popular, they have given rise to rampant concerns regarding user privacy. This work investigates machine learning techniques to extract ostensibly private information from the TCP traffic moving between an Echo device and Amazon's servers, despite the fact that all such traffic is encrypted. Specifically, we investigate a supervised classification problem using six machine learning algorithms and three feature vectors. Our "request type classification" problem seeks to determine what type of user request is being answered by the Echo (again, even though the requests are encrypted). With six classes, we achieve 97% accuracy in this task using random forests.
机译:由于像亚马逊回声一样的智能扬声器变得更受欢迎,因此他们对用户隐私的兴趣造成了猖獗的担忧。尽管所有此类流量都是加密的事实,但这项工作调查了从回波设备和亚马逊服务器之间移动的TCP流量中提取了外翻的私人信息。具体而言,我们使用六种机器学习算法和三个特征向量调查监督分类问题。我们的“请求类型分类”问题旨在确定回声(再次)回答哪种类型的用户请求,即使请求是加密的)。有六级,我们使用随机森林在此任务中获得了97%的准确性。

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