Search for: All records

Extensive recent research has shown that it is surprisingly easy to infer Amazon Alexa voice commands over their network traffic data. To prevent these traffic analytics (TA)-based inference attacks, smart home owners are considering deploying virtual private networks (VPNs) to safeguard their smart speakers. In this work, we design a new machine learning-powered attack framework—VoiceAttack that could still accurately fingerprint voice commands on VPN-encrypted voice speaker network traffic. We evaluate VoiceAttack under 5 different real-world settings using Amazon Alexa and Google Home. Our results show that VoiceAttack could correctly infer voice command sentences with a Matthews Correlation Coefficient (MCC) of 0.68 in a closed-world setting and infer voice command categories with an MCC of 0.84 in an open-world setting by eavesdropping VPN-encrypted network traffic data. This presents a significant risk to user privacy and security, as it suggests that external on-path attackers could still potentially intercept and decipher users’ voice commands despite the VPN encryption. We then further examine the sensitivity of voice speaker commands to VoiceAttack. We find that 134 voice speaker commands are highly vulnerable to VoiceAttack. We also present a defense approach—VoiceDefense, which could inject inject appropriate traffic “noise” into voice speaker traffic. And our evaluation results show that VoiceDefense could effectively mitigate VoiceAttack on Amazon Echo and Google Home.