Fingerprinting encrypted voice traffic on smart speakers with deep learning

Wang，Chenggang1; Kennedy，Sean1; Li，Haipeng1; Hudson，King1; Atluri，Gowtham1; Wei，Xuetao2; Sun，Wenhai3; Wang，Boyang1

10.1145/3395351.3399357

2020-07-08

WiSec 2020 - Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks

254-265

This paper investigates the privacy leakage of smart speakers under an encrypted traffic analysis attack, referred to as voice command fingerprinting. In this attack, an adversary can eavesdrop both outgoing and incoming encrypted voice traffic of a smart speaker, and infers which voice command a user says over encrypted traffic. We first built an automatic voice traffic collection tool and collected two large-scale datasets on two smart speakers, Amazon Echo and Google Home. Then, we implemented proof-of-concept attacks by leveraging deep learning. Our experimental results over the two datasets indicate disturbing privacy concerns. Specifically, compared to 1% accuracy with random guess, our attacks can correctly infer voice commands over encrypted traffic with 92.89% accuracy on Amazon Echo. Despite variances that human voices may cause on outgoing traffic, our proof-of-concept attacks remain effective even only leveraging incoming traffic (i.e., the traffic from the server). This is because the AI-based voice services running on the server side response commands in the same voice and with a deterministic or predictable manner in text, which leave distinguishable pattern over encrypted traffic. We also built a proof-of-concept defense to obfuscate encrypted traffic. Our results show that the defense can effectively mitigate attack accuracy on Amazon Echo to 32.18%.

encrypted traffic analysis machine learning smart speaker

其他

英语

EI

20204109313622

Cellular radio systems ; Cryptography ; Large dataset ; Mobile telecommunication systems ; Network security ; Wireless networks

Radio Systems and Equipment:716.3 ; Computer Software, Data Handling and Applications:723

2-s2.0-85091981459

Scopus

1.University of Cincinnati,United States
2.Southern University of Science and Technology,China
3.Purdue University,United States

Wang，Chenggang,Kennedy，Sean,Li，Haipeng,et al. Fingerprinting encrypted voice traffic on smart speakers with deep learning[C],2020:254-265.