Amazon Echo Security: Machine Learning to Classify Encrypted Traffic
Ryan Blake Jackson, Tracy K. Camp · 2018
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.