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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.
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This content will become publicly available on May 12, 2026
Fingerprinting Voice Commands of VPN-protected Smart Speakers
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.
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- Award ID(s):
- 2238701
- PAR ID:
- 10596256
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Sensor Networks
- ISSN:
- 1550-4859
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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