Home > Research > Publications & Outputs > P4ID

Research

Associated organisational units

Electronic data

  • P4_IDS_Paper

    Rights statement: ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 125 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

P4ID: P4 Enhanced Intrusion Detection

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

P4ID: P4 Enhanced Intrusion Detection. / Lewis, Benjamin; Broadbent, Matthew; Race, Nicholas.
2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 2020. p. 1-4.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Lewis, B, Broadbent, M & Race, N 2020, P4ID: P4 Enhanced Intrusion Detection. in 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, pp. 1-4. https://doi.org/10.1109/NFV-SDN47374.2019.9040044

APA

Lewis, B., Broadbent, M., & Race, N. (2020). P4ID: P4 Enhanced Intrusion Detection. In 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) (pp. 1-4). IEEE. https://doi.org/10.1109/NFV-SDN47374.2019.9040044

Vancouver

Lewis B, Broadbent M, Race N. P4ID: P4 Enhanced Intrusion Detection. In 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE. 2020. p. 1-4 doi: 10.1109/NFV-SDN47374.2019.9040044

Author

Lewis, Benjamin ; Broadbent, Matthew ; Race, Nicholas. / P4ID : P4 Enhanced Intrusion Detection. 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 2020. pp. 1-4

Bibtex

@inproceedings{ff165fa500864e5582bbcde539cb162f,
title = "P4ID: P4 Enhanced Intrusion Detection",
abstract = "The growth in scale and capacity of networks in recent years leads to challenges of positioning and scalability of Intrusion Detection Systems (IDS). With the flexibility afforded by programmable dataplanes, it is now possible to perform a new level of intrusion detection in switches themselves. We presentP4ID, combining a rule parser, stateless and stateful packet processing using P4, and evaluate it using publicly available datasets. We show that using this technique, we can achieve a significant reduction in traffic being processed by an IDS.",
author = "Benjamin Lewis and Matthew Broadbent and Nicholas Race",
note = "{\textcopyright}2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2020",
month = mar,
day = "19",
doi = "10.1109/NFV-SDN47374.2019.9040044",
language = "English",
pages = "1--4",
booktitle = "2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - P4ID

T2 - P4 Enhanced Intrusion Detection

AU - Lewis, Benjamin

AU - Broadbent, Matthew

AU - Race, Nicholas

N1 - ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2020/3/19

Y1 - 2020/3/19

N2 - The growth in scale and capacity of networks in recent years leads to challenges of positioning and scalability of Intrusion Detection Systems (IDS). With the flexibility afforded by programmable dataplanes, it is now possible to perform a new level of intrusion detection in switches themselves. We presentP4ID, combining a rule parser, stateless and stateful packet processing using P4, and evaluate it using publicly available datasets. We show that using this technique, we can achieve a significant reduction in traffic being processed by an IDS.

AB - The growth in scale and capacity of networks in recent years leads to challenges of positioning and scalability of Intrusion Detection Systems (IDS). With the flexibility afforded by programmable dataplanes, it is now possible to perform a new level of intrusion detection in switches themselves. We presentP4ID, combining a rule parser, stateless and stateful packet processing using P4, and evaluate it using publicly available datasets. We show that using this technique, we can achieve a significant reduction in traffic being processed by an IDS.

U2 - 10.1109/NFV-SDN47374.2019.9040044

DO - 10.1109/NFV-SDN47374.2019.9040044

M3 - Conference contribution/Paper

SP - 1

EP - 4

BT - 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)

PB - IEEE

ER -