Honeynet-based collaborative defense using improved highly predictive blacklisting algorithm

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

7 Scopus citations

Abstract

We present a honeynet-based collaborative defense framework and an improved highly predictive blacklisting algorithm is developed to generate highly personalized and predictive blacklists for individual networks by correlating historic attackers captured by honeynet deployed in each network. In this way, different networks can defend new attackers in a collaborative way because one network will notify another network, by dint of honeynet, of the most probable attackers in the near future based on their historic correlation. A relatively proactive defense strategy is realized based on honeynet in a collaborative way and we evaluated our algorithm with real-world honeynet traces captured in different subnets. The results show our method can generate highly personalized and predictive blacklists for individual networks with a high hit rate and defense rate.

Original languageEnglish
Title of host publication2010 8th World Congress on Intelligent Control and Automation, WCICA 2010
Pages1283-1288
Number of pages6
DOIs
StatePublished - 2010
Event2010 8th World Congress on Intelligent Control and Automation, WCICA 2010 - Jinan, China
Duration: 7 Jul 20109 Jul 2010

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)

Conference

Conference2010 8th World Congress on Intelligent Control and Automation, WCICA 2010
Country/TerritoryChina
CityJinan
Period7/07/109/07/10

Keywords

  • Blacklist
  • Collaborative Defense
  • Honeynet
  • Network Security

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