P4M3: Preventing SYN Flood Attacks on IPv6 Networks in SDN Using P4

Software-Defined Networking (SDN) centralizes control through a controller, making it a target for DDoS attacks, especially with IPv6's expansive address space, which increases vulnerability to SYN Flood attacks. P4M3(Three-layer Module architecture based on P4), a preventive solution for IPv6 SDN environments, is designed to detect and prevent SYN Flood attacks through P4 switches and the ONOS controller. The solution includes three modules: the Quick Detection and Threshold Detection modules on the data plane, and the Machine Learning Detection module on the controller. The data plane uses a danger address matching table and a Count-Min Sketch threshold method to filter suspicious SYN Flood traffic and relay packet characteristics to the controller's Machine Learning Detection module. The controller classifies packets using an ensemble learning model and issues blocking rules to the switch as needed. Results show that P4M3 reduces CPU usage by 81.92%, while maintaining low latency and enhancing SYN Flood detection accuracy, thus demonstrating its effectiveness in protecting IPv6 SDN networks from DDoS attacks.

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Publication Details

Published
2025-05-05
DOI
https://doi.org/10.1109/cscwd64889.2025.11033226
Citations
1
Primary Topic
Software-Defined Networks and 5G
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article
Field-Weighted Citation Impact
1.49
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article

P4M3: Preventing SYN Flood Attacks on IPv6 Networks in SDN Using P4

Shu Li, Qingyun Liu, Hongfei Zhang, Jie Li et al.
1 citations
Software-Defined Networks and 5G
1.49
article

P4M3: Preventing SYN Flood Attacks on IPv6 Networks in SDN Using P4

Shu Li, Qingyun Liu, Hongfei Zhang, Jie Li, Huiping Zhu, Zeyu Yang
article en
1 citations

Abstract

Software-Defined Networking (SDN) centralizes control through a controller, making it a target for DDoS attacks, especially with IPv6's expansive address space, which increases vulnerability to SYN Flood attacks. P4M3(Three-layer Module architecture based on P4), a preventive solution for IPv6 SDN environments, is designed to detect and prevent SYN Flood attacks through P4 switches and the ONOS controller. The solution includes three modules: the Quick Detection and Threshold Detection modules on the data plane, and the Machine Learning Detection module on the controller. The data plane uses a danger address matching table and a Count-Min Sketch threshold method to filter suspicious SYN Flood traffic and relay packet characteristics to the controller's Machine Learning Detection module. The controller classifies packets using an ensemble learning model and issues blocking rules to the switch as needed. Results show that P4M3 reduces CPU usage by 81.92%, while maintaining low latency and enhancing SYN Flood detection accuracy, thus demonstrating its effectiveness in protecting IPv6 SDN networks from DDoS attacks.

Chinese Academy of Sciences (CN), Institute of Information Engineering (CN)
Climate action
Openalex Percentile: Top 16%
Software-Defined Networks and 5G
1.49
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P4M3: Preventing SYN Flood Attacks on IPv6 Networks in SDN Using P4 — Shu Li, Qingyun Liu, et al. · (2025) | TGRS Research Map | TGRS