VoipLoc: VoIP call provenance using acoustic side-channels

Research output: Contribution to conferencePaper

Abstract

We develop a novel technique to determine call provenance in anonymous VoIP communications using acoustic side-channels. The technique exploits location-attributable information embedded within audio speech data. The victim’s speech is exploited as an excitation signal, which is modulated (acted upon) by the acoustic reflection characteristics of the victim’s location. We show that leading VoIP communication channels faithfully transfer this information between sender-receiver pairs, enabling passive receivers to extract a location fingerprint, to establish call provenance. To establish provenance, a fingerprint is compared against a database of labelled fingerprints to identify a match. The technique is fully passive and does not depend on any characteristic background sounds, is speaker independent, and is robust to lossy network conditions. Evaluation using a corpus of recordings of VoIP conversations, over the Tor network, confirms that recording locations can be fingerprinted and detected remotely with low false-positive rate.

Conference

ConferenceIEEE Security and Privacy 2020
CountryUnited States
CitySan Francisco
Period18/05/2020/05/20

Fingerprint

Acoustics
Acoustic waves
Communication

Keywords

  • VoIP
  • voice over IP
  • anonymous communication channels
  • security
  • audio segmentation

Cite this

Nagaraja, S., & Shah, R. (2019). VoipLoc: VoIP call provenance using acoustic side-channels. Paper presented at IEEE Security and Privacy 2020, San Francisco, United States.
Nagaraja, Shishir ; Shah, Ryan. / VoipLoc : VoIP call provenance using acoustic side-channels. Paper presented at IEEE Security and Privacy 2020, San Francisco, United States.
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title = "VoipLoc: VoIP call provenance using acoustic side-channels",
abstract = "We develop a novel technique to determine call provenance in anonymous VoIP communications using acoustic side-channels. The technique exploits location-attributable information embedded within audio speech data. The victim’s speech is exploited as an excitation signal, which is modulated (acted upon) by the acoustic reflection characteristics of the victim’s location. We show that leading VoIP communication channels faithfully transfer this information between sender-receiver pairs, enabling passive receivers to extract a location fingerprint, to establish call provenance. To establish provenance, a fingerprint is compared against a database of labelled fingerprints to identify a match. The technique is fully passive and does not depend on any characteristic background sounds, is speaker independent, and is robust to lossy network conditions. Evaluation using a corpus of recordings of VoIP conversations, over the Tor network, confirms that recording locations can be fingerprinted and detected remotely with low false-positive rate.",
keywords = "VoIP, voice over IP, anonymous communication channels, security, audio segmentation",
author = "Shishir Nagaraja and Ryan Shah",
year = "2019",
month = "7",
day = "31",
language = "English",
note = "IEEE Security and Privacy 2020 ; Conference date: 18-05-2020 Through 20-05-2020",

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Nagaraja, S & Shah, R 2019, 'VoipLoc: VoIP call provenance using acoustic side-channels' Paper presented at IEEE Security and Privacy 2020, San Francisco, United States, 18/05/20 - 20/05/20, .

VoipLoc : VoIP call provenance using acoustic side-channels. / Nagaraja, Shishir; Shah, Ryan.

2019. Paper presented at IEEE Security and Privacy 2020, San Francisco, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - VoipLoc

T2 - VoIP call provenance using acoustic side-channels

AU - Nagaraja, Shishir

AU - Shah, Ryan

PY - 2019/7/31

Y1 - 2019/7/31

N2 - We develop a novel technique to determine call provenance in anonymous VoIP communications using acoustic side-channels. The technique exploits location-attributable information embedded within audio speech data. The victim’s speech is exploited as an excitation signal, which is modulated (acted upon) by the acoustic reflection characteristics of the victim’s location. We show that leading VoIP communication channels faithfully transfer this information between sender-receiver pairs, enabling passive receivers to extract a location fingerprint, to establish call provenance. To establish provenance, a fingerprint is compared against a database of labelled fingerprints to identify a match. The technique is fully passive and does not depend on any characteristic background sounds, is speaker independent, and is robust to lossy network conditions. Evaluation using a corpus of recordings of VoIP conversations, over the Tor network, confirms that recording locations can be fingerprinted and detected remotely with low false-positive rate.

AB - We develop a novel technique to determine call provenance in anonymous VoIP communications using acoustic side-channels. The technique exploits location-attributable information embedded within audio speech data. The victim’s speech is exploited as an excitation signal, which is modulated (acted upon) by the acoustic reflection characteristics of the victim’s location. We show that leading VoIP communication channels faithfully transfer this information between sender-receiver pairs, enabling passive receivers to extract a location fingerprint, to establish call provenance. To establish provenance, a fingerprint is compared against a database of labelled fingerprints to identify a match. The technique is fully passive and does not depend on any characteristic background sounds, is speaker independent, and is robust to lossy network conditions. Evaluation using a corpus of recordings of VoIP conversations, over the Tor network, confirms that recording locations can be fingerprinted and detected remotely with low false-positive rate.

KW - VoIP

KW - voice over IP

KW - anonymous communication channels

KW - security

KW - audio segmentation

M3 - Paper

ER -

Nagaraja S, Shah R. VoipLoc: VoIP call provenance using acoustic side-channels. 2019. Paper presented at IEEE Security and Privacy 2020, San Francisco, United States.