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.
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Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
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 -