Gao, S;
Handley, M;
Vissicchio, S;
(2021)
Stats 101 in P4: Towards In-Switch Anomaly Detection.
In:
HotNets '21: Proceedings of the Twentieth ACM Workshop on Hot Topics in Networks.
(pp. pp. 84-90).
ACM: Virtual Event, United Kingdom.
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Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | Stats 101 in P4: Towards In-Switch Anomaly Detection |
| Event: | 20th ACM Workshop on Hot Topics in Networks |
| ISBN-13: | 9781450390873 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3484266.3487370 |
| Publisher version: | https://doi.org/10.1145/3484266.3487370 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. https://doi.org/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10139890 |
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