eprintid: 10139890
rev_number: 12
eprint_status: archive
userid: 608
dir: disk0/10/13/98/90
datestamp: 2021-12-08 10:32:50
lastmod: 2021-12-08 10:32:50
status_changed: 2021-12-08 10:32:50
type: proceedings_section
metadata_visibility: show
creators_name: Gao, S
creators_name: Handley, M
creators_name: Vissicchio, S
title: Stats 101 in P4: Towards In-Switch Anomaly Detection
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
https://doi.org/
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.
date: 2021-11-10
date_type: published
publisher: ACM
official_url: https://doi.org/10.1145/3484266.3487370
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1908095
doi: 10.1145/3484266.3487370
isbn_13: 9781450390873
lyricists_name: Handley, Mark
lyricists_name: Vissicchio, Stefano
lyricists_id: MJHAN69
lyricists_id: SVISS67
actors_name: Vissicchio, Stefano
actors_id: SVISS67
actors_role: owner
full_text_status: public
publication: HotNets 2021 - Proceedings of the 20th ACM Workshop on Hot Topics in Networks
place_of_pub: Virtual Event, United Kingdom
pagerange: 84-90
event_title: 20th ACM Workshop on Hot Topics in Networks
book_title: HotNets '21: Proceedings of the Twentieth ACM Workshop on Hot Topics in Networks
citation:        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.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10139890/1/Stat4_hotnets21.pdf