Stats 101 in P4: Towards In-Switch Anomaly Detection

被引:7
作者
Gao, Sam [1 ]
Handley, Mark [1 ]
Vissicchio, Stefano [1 ]
机构
[1] UCL, London, England
来源
PROCEEDINGS OF THE THE 20TH ACM WORKSHOP ON HOT TOPICS IN NETWORKS, HOTNETS 2021 | 2021年
关键词
D O I
10.1145/3484266.3487370
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Data plane programmability is greatly improving network monitoring. Most new proposals rely on controllers pulling information (e.g., sketches or packets) from the data plane. This architecture is not a good fit for tasks requiring high reactivity, such as failure recovery, attack mitigation, and so on. Focusing on these tasks, we argue for a different architecture, where the data plane autonomously detects anomalies and pushes alerts to the controller. As a first step, we demonstrate that statistical checks can be implemented in P4 by revisiting definition and online computation of statistical measures. We collect our techniques in a P4 library, and showcase how they enable in-switch anomaly detection.
引用
收藏
页码:84 / 90
页数:7
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