research-article

Mass surveillance of VoIP calls in the data plane

Authors:

Maria Apostolaki,

Laurent VanbeverAuthors Info & Claims

SOSR '22: Proceedings of the Symposium on SDN Research

Pages 33 - 49

https://doi.org/10.1145/3563647.3563649

Published: 19 October 2022 Publication History

Abstract

Over the last decade, programmable data planes have enabled highly customizable and efficient packet processing in commercial off-the-shelf hardware. Although researchers have demonstrated various use cases of this technology, its potential misuse has gained much less traction. This work investigates a typical surveillance scenario, VoIP call identification and monitoring, through a tailored data-plane attack.

We introduce DELTA, a network-level side-channel attack that can efficiently identify VoIP calls and their hosting services. DELTA achieves this by tracking the inherent network footprint of VoIP services in the data plane. Specifically, DELTA stores the user addresses recently connected to VoIP services and links potential call flows with these addresses.

We implement DELTA on existing hardware and conduct high-throughput tests based on representative traffic. DELTA can simultaneously store around 100 000 VoIP connections per service and identify call streams in-path, at line-rate, inside terabits of Internet traffic per second, immediately revealing users' communication patterns.

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Cited By

Gong FRaghunathan DGupta AApostolaki M(2023)Towards Integrating Formal Methods into ML-Based Systems for NetworkingProceedings of the 22nd ACM Workshop on Hot Topics in Networks10.1145/3626111.3628188(48-55)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1145/3626111.3628188

Index Terms

Mass surveillance of VoIP calls in the data plane
1. Networks
  1. Network services
    1. Programmable networks

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Gong FRaghunathan DGupta AApostolaki M(2023)Towards Integrating Formal Methods into ML-Based Systems for NetworkingProceedings of the 22nd ACM Workshop on Hot Topics in Networks10.1145/3626111.3628188(48-55)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1145/3626111.3628188

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