research-article

Heimdallr: Fingerprinting SD-WAN Control-Plane Architecture via Encrypted Control Traffic

Authors:

Myoungsung You,

Jinwoo KimAuthors Info & Claims

ACSAC '22: Proceedings of the 38th Annual Computer Security Applications Conference

Pages 949 - 963

https://doi.org/10.1145/3564625.3564642

Published: 05 December 2022 Publication History

Abstract

Software-defined wide area network (SD-WAN) has emerged as a new paradigm for steering a large-scale network flexibly by adopting distributed software-defined network (SDN) controllers. The key to building a logically centralized but physically distributed control-plane is running diverse cluster management protocols to achieve consistency through an exchange of control traffic. Meanwhile, we observe that the control traffic exposes unique time-series patterns and directional relationships due to the operational structure even though the traffic is encrypted, and this pattern can disclose confidential information such as control-plane topology and protocol dependencies, which can be exploited for severe attacks. With this insight, we propose a new SD-WAN fingerprinting system, called Heimdallr. It analyzes periodical and operational patterns of SD-WAN cluster management protocols and the context of flow directions from the collected control traffic utilizing a deep learning-based approach, so that it can classify the cluster management protocols automatically from miscellaneous control traffic datasets. Our evaluation, which is performed in a realistic SD-WAN environment consisting of geographically distant three campus networks and one enterprise network shows that Heimdallr can classify SD-WAN control traffic with ≥ 93%, identify individual protocols with ≥ 80% macro F-1 scores, and finally can infer control-plane topology with ≥ 70% similarity.

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Heimdallr: Fingerprinting SD-WAN Control-Plane Architecture via Encrypted Control Traffic
1. Computing methodologies
  1. Machine learning

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ACSAC '22: Proceedings of the 38th Annual Computer Security Applications Conference

December 2022

1021 pages

ISBN:9781450397599

DOI:10.1145/3564625

Copyright © 2022 ACM.

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Published: 05 December 2022

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National Research Foundation of Korea

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ACSAC

ACSAC: Annual Computer Security Applications Conference

December 5 - 9, 2022

TX, Austin, USA

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Overall Acceptance Rate 104 of 497 submissions, 21%

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Cited By

Kim JSeo MMarin ELee SNam JShin S(2024) Ambusher : Exploring the Security of Distributed SDN Controllers Through Protocol State Fuzzing IEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.340296719(6264-6279)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3402967
Kim JSong MSeo MJin YShin S(2024)PassREfinder: Credential Stuffing Risk Prediction by Representing Password Reuse between Websites on a Graph2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00020(1385-1404)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00020
Seo MKim JYou MShin SKim J(2024)gShock: A GNN-Based Fingerprinting System for Permissioned Blockchain Networks Over Encrypted ChannelsIEEE Access10.1109/ACCESS.2024.346958312(146328-146342)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3469583
Kim JSeo MLee SNam JYegneswaran VPorras PGu GShin S(2024)Enhancing security in SDNComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110203241:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110203
Navarro ACanonico RBotta A(2023)Software Defined Wide Area Networks: Current Challenges and Future Perspectives2023 IEEE 9th International Conference on Network Softwarization (NetSoft)10.1109/NetSoft57336.2023.10175458(350-353)Online publication date: 19-Jun-2023
https://doi.org/10.1109/NetSoft57336.2023.10175458

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