Optimum identification of worm-infected hosts

Noriaki Kamiyama, Tatsuya Mori, Ryoichi Kawahara, Shigeaki Harada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The authors have proposed a method of identifying superspreaders by flow sampling and a method of extracting worm-infected hosts from the identified superspreaders using a white list. However, the problem of how to optimally set parameters, φ, the measurement period length, m *, the identification threshold of the flow count m within φ, and H *, the identification probability for hosts with m∈=∈m *, remains unsolved. These three parameters seriously affect the worm-spreading property. In this paper, we propose a method of optimally designing these three parameters to satisfy the condition that the ratio of the number of active worm-infected hosts divided by the number of all the vulnerable hosts is bound by a given upper-limit during the time T required to develop a patch or an anti-worm vaccine.

Original languageEnglish
Title of host publicationIP Operations and Management - 8th IEEE International Workshop, IPOM 2008, Proceedings
Pages103-116
Number of pages14
DOIs
Publication statusPublished - 2008 Nov 28
Event8th IEEE International Workshop on IP Operations and Management, IPOM 2008 - Samos Island, Greece
Duration: 2008 Sep 222008 Sep 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5275 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th IEEE International Workshop on IP Operations and Management, IPOM 2008
CountryGreece
CitySamos Island
Period08/9/2208/9/26

Keywords

  • Detection
  • Optimum design
  • Sampling
  • Worm

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kamiyama, N., Mori, T., Kawahara, R., & Harada, S. (2008). Optimum identification of worm-infected hosts. In IP Operations and Management - 8th IEEE International Workshop, IPOM 2008, Proceedings (pp. 103-116). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5275 LNCS). https://doi.org/10.1007/978-3-540-87357-0_9