PCL: Packet classification with limited knowledge

Vitalii Demianiuk, Chen Hajaj, Kirill Kogan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


We introduce a novel representation of packet classifiers allowing to operate on partially available input data varying dynamically. For a given packet classifier, availability of fields or complexity of field computations, and free target specific resources, the proposed infrastructure computes a classifier representation satisfying performance and robustness requirements. We show the feasibility to reconstruct a classification result in this noisy environment, allowing for the improvement of performance and the achievement of additional robustness levels of network infrastructure. Our results are supported by extensive evaluations in various settings where only a partial input is available.

Original languageEnglish
Title of host publicationINFOCOM 2021 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738112817
StatePublished - 10 May 2021
Externally publishedYes
Event40th IEEE Conference on Computer Communications, INFOCOM 2021 - Vancouver, Canada
Duration: 10 May 202113 May 2021

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Conference40th IEEE Conference on Computer Communications, INFOCOM 2021

Bibliographical note

Funding Information:
Acknowledgments The work of Vitalii Demianiuk and Kirill Kogan was supported in part by the Israeli Innovation Authority under the Knowledge Transfer Commercialization Program (MAGNETON) file no. 71249, in part by the Ariel Cyber Innovation Center in cooperation with the Israel National Cyber Directorate in the Prime Minister’s Office. In addition, this work was supported by the Data Science and Artificial Intelligence Research Center at Ariel University.

Publisher Copyright:
© 2021 IEEE.


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