Robust Online Learning against Malicious Manipulation with Application to Network Flow Classification

Yupeng Li, Ben Liang, Ali Tizghadam

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

5 Citations (Scopus)

Abstract

Malicious data manipulation reduces the effectiveness of machine learning techniques, which rely on accurate knowledge of the input data. Motivated by real-world applications in network flow classification, we address the problem of robust online learning with delayed feedback in the presence of malicious data generators that attempt to gain favorable classification outcome by manipulating the data features. We propose online algorithms termed ROLC-NC and ROLC-C when the malicious data generators are non-clairvoyant and clairvoyant, respectively. We derive regret bounds for both algorithms and show that they are sub-linear under mild conditions. We further evaluate the proposed algorithms in network flow classification via extensive experiments using real-world data traces. Our experimental results demonstrate that both algorithms can approach the performance of an optimal static offline classifier that is not under attack, while outperforming the same offline classifier when tested with a mixture of normal and manipulated data.

Original languageEnglish
Title of host publicationIEEE INFOCOM 2021 - IEEE Conference on Computer Communications
PublisherIEEE
Pages1-10
Number of pages10
ISBN (Electronic)9781665403252, 9780738112817
ISBN (Print)9781665431316
DOIs
Publication statusPublished - 10 May 2021
Event40th IEEE Conference on Computer Communications, IEEE INFOCOM 2021 - Vancouver, BC, Canada
Duration: 10 May 202113 May 2021
https://infocom2021.ieee-infocom.org/ (Conference website)
https://ieeexplore.ieee.org/xpl/conhome/9488422/proceeding (Conference proceedings)

Publication series

NameProceedings - IEEE INFOCOM
Volume2021-May
ISSN (Print)0743-166X
ISSN (Electronic)2641-9874

Conference

Conference40th IEEE Conference on Computer Communications, IEEE INFOCOM 2021
Country/TerritoryCanada
CityVancouver, BC
Period10/05/2113/05/21
Internet address

Scopus Subject Areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

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