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P4Pir: in-network analysis for smart IoT gateways

Authors:

Changgang Zheng,

Radostin Stoyanov,

Noa ZilbermanAuthors Info & Claims

SIGCOMM '22: Proceedings of the SIGCOMM '22 Poster and Demo Sessions

Pages 46 - 48

https://doi.org/10.1145/3546037.3546060

Published: 25 October 2022 Publication History

Abstract

IoT gateways are vital to the scalability and security of IoT networks. As more devices connect to the network, traditional hard-coded gateways fail to flexibly process diverse IoT traffic from highly dynamic devices. This calls for a more advanced analysis solution. In this work, we present P4Pir, an in-network traffic analysis solution for IoT gateways. It utilizes programmable data planes for in-band traffic learning with self-driven machine learning model updates. Preliminary results show that P4Pir can accurately detect emerging attacks based on retraining and updating the machine learning model.

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Cited By

Zheng CZang MHong XPerreault LBensoussane RVargaftik SBen-Itzhak YZilberman N(2024)Planter: Rapid Prototyping of In-Network Machine Learning InferenceACM SIGCOMM Computer Communication Review10.1145/3687230.368723254:1(2-21)Online publication date: 6-Aug-2024
https://doi.org/10.1145/3687230.3687232
Madarasingha CDahanayaka TThilakarathna KSeneviratne SLee YKanhere SSeneviratne ARidley PSekar VYu MSeneviratne AVeitch D(2024)Demo: P4Xtnd: P4 Programmability on Resource Constrained Environments with Extended Network FunctionalitiesProceedings of the ACM SIGCOMM 2024 Conference: Posters and Demos10.1145/3672202.3673731(104-106)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3672202.3673731
Seufert MDietz KWehner NGeißler SSchüler JWolz MHotho ACasas PHoßfeld TFeldmann A(2024) Marina : Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale IEEE Transactions on Network and Service Management10.1109/TNSM.2024.338239321:3(2773-2790)Online publication date: Jun-2024
https://doi.org/10.1109/TNSM.2024.3382393
Show More Cited By

Index Terms

P4Pir: in-network analysis for smart IoT gateways

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Published In

cover image ACM Conferences

SIGCOMM '22: Proceedings of the SIGCOMM '22 Poster and Demo Sessions

August 2022

69 pages

ISBN:9781450394345

DOI:10.1145/3546037

Copyright © 2022 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGCOMM: ACM Special Interest Group on Data Communication

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2022

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SIGCOMM '22

Sponsor:

SIGCOMM

SIGCOMM '22: ACM SIGCOMM 2022 Conference

August 22 - 26, 2022

Amsterdam, Netherlands

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Overall Acceptance Rate 92 of 158 submissions, 58%

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Cited By

Zheng CZang MHong XPerreault LBensoussane RVargaftik SBen-Itzhak YZilberman N(2024)Planter: Rapid Prototyping of In-Network Machine Learning InferenceACM SIGCOMM Computer Communication Review10.1145/3687230.368723254:1(2-21)Online publication date: 6-Aug-2024
https://doi.org/10.1145/3687230.3687232
Madarasingha CDahanayaka TThilakarathna KSeneviratne SLee YKanhere SSeneviratne ARidley PSekar VYu MSeneviratne AVeitch D(2024)Demo: P4Xtnd: P4 Programmability on Resource Constrained Environments with Extended Network FunctionalitiesProceedings of the ACM SIGCOMM 2024 Conference: Posters and Demos10.1145/3672202.3673731(104-106)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3672202.3673731
Seufert MDietz KWehner NGeißler SSchüler JWolz MHotho ACasas PHoßfeld TFeldmann A(2024) Marina : Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale IEEE Transactions on Network and Service Management10.1109/TNSM.2024.338239321:3(2773-2790)Online publication date: Jun-2024
https://doi.org/10.1109/TNSM.2024.3382393
Zheng CHong XDing DVargaftik SBen-Itzhak YZilberman N(2024)In-Network Machine Learning Using Programmable Network Devices: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.334435126:2(1171-1200)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2023.3344351
Datta SU. V(2024)PiGatewayComputer Communications10.1016/j.comcom.2023.11.019213:C(309-319)Online publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.comcom.2023.11.019
Zang MZheng CKoziak TZilberman NDittmann L(2023)Federated Learning-Based In-Network Traffic Analysis on IoT Edge2023 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking57963.2023.10186438(1-6)Online publication date: 12-Jun-2023
https://doi.org/10.23919/IFIPNetworking57963.2023.10186438
Hong XZheng CZohren SZilberman N(2023)LOBIN: In-Network Machine Learning for Limit Order Books2023 IEEE 24th International Conference on High Performance Switching and Routing (HPSR)10.1109/HPSR57248.2023.10147958(159-166)Online publication date: 5-Jun-2023
https://doi.org/10.1109/HPSR57248.2023.10147958

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