research-article

Classification of Encrypted IoT Traffic despite Padding and Shaping

Authors:

Aviv Engelberg and

Avishai WoolAuthors Info & Claims

WPES'22: Proceedings of the 21st Workshop on Privacy in the Electronic Society

November 2022

Pages 1 - 13

https://doi.org/10.1145/3559613.3563191

Published: 07 November 2022 Publication History

Abstract

It is well-known that when IoT traffic is unencrypted it is possible to identify the active devices based on their TCP/IP headers. And when traffic is encrypted, packet-sizes and timings can still be used to do so. To defend against such fingerprinting, traffic padding and shaping were introduced. In this paper we show that even with these mitigations, the privacy of IoT consumers can still be violated. The main tool we use in our analysis is the full distribution of packet-size---as opposed to commonly used statistics such as mean and variance. We evaluate the performance of a local adversary, such as a snooping neighbor or a criminal, against 8~different padding methods. We show that our classifiers achieve perfect (100% accuracy) classification using the full packet-size distribution for low-overhead methods, whereas prior works that rely on statistical metadata achieved lower rates even when no padding and shaping were used. We also achieve an excellent classification rate even against high-overhead methods. We further show how an external adversary such as a malicious ISP or a government intelligence agency, who only sees the padded and shaped traffic as it goes through a VPN, can accurately identify the subset of active devices with Recall and Precision of at least 96%. Finally, we also propose a new method of padding we call the Dynamic STP (DSTP) that incurs significantly less per-packet overhead compared to other padding methods we tested and guarantees more privacy to IoT consumers.

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Cited By

Okui NNakahara MKubota A(2024)Survey and Experimentation to Compare IoT Device Model Identification Methods2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)10.1109/WoWMoM60985.2024.00014(13-17)Online publication date: 4-Jun-2024
https://doi.org/10.1109/WoWMoM60985.2024.00014
Ren YWang JLi HZhu HSun LChen HDuh WHuang HKato MMothe JPoblete B(2023)DeviceGPT: A Generative Pre-Training Transformer on the Heterogenous Graph for Internet of ThingsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591972(1929-1933)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591972
Cheng YGraves ESwami ASabharwal A(2023)Estimating Traffic Rates in CSMA/CA Networks: A Feasibility Analysis for a Class of EavesdroppersIEEE Transactions on Wireless Communications10.1109/TWC.2023.327355022:12(9793-9807)Online publication date: 11-May-2023
https://dl.acm.org/doi/10.1109/TWC.2023.3273550
Show More Cited By

Index Terms

Classification of Encrypted IoT Traffic despite Padding and Shaping
1. Networks
  1. Network properties
    1. Network privacy and anonymity
2. Security and privacy
  1. Network security
    1. Mobile and wireless security

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cover image ACM Conferences

WPES'22: Proceedings of the 21st Workshop on Privacy in the Electronic Society

November 2022

227 pages

ISBN:9781450398732

DOI:10.1145/3559613

Program Chairs:
Yuan Hong
University of Connecticut, USA
,
Lingyu Wang
Concordia University, Canada

Copyright © 2022 ACM.

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Published: 07 November 2022

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Cited By

Okui NNakahara MKubota A(2024)Survey and Experimentation to Compare IoT Device Model Identification Methods2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)10.1109/WoWMoM60985.2024.00014(13-17)Online publication date: 4-Jun-2024
https://doi.org/10.1109/WoWMoM60985.2024.00014
Ren YWang JLi HZhu HSun LChen HDuh WHuang HKato MMothe JPoblete B(2023)DeviceGPT: A Generative Pre-Training Transformer on the Heterogenous Graph for Internet of ThingsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591972(1929-1933)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591972
Cheng YGraves ESwami ASabharwal A(2023)Estimating Traffic Rates in CSMA/CA Networks: A Feasibility Analysis for a Class of EavesdroppersIEEE Transactions on Wireless Communications10.1109/TWC.2023.327355022:12(9793-9807)Online publication date: 11-May-2023
https://dl.acm.org/doi/10.1109/TWC.2023.3273550
Pashamokhtari AOkui NMiyake YNakahara MGharakheili H(2023)Combining Stochastic and Deterministic Modeling of IPFIX Records to Infer Connected IoT Devices in Residential ISP NetworksIEEE Internet of Things Journal10.1109/JIOT.2022.322211610:6(5128-5145)Online publication date: 15-Mar-2023
https://doi.org/10.1109/JIOT.2022.3222116
Fu HSun MHe BLi JZhu X(2023)A Survey of Traffic Shaping Technology in Internet of ThingsIEEE Access10.1109/ACCESS.2022.323339411(3794-3809)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2022.3233394

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