Visual-based anomaly detection for BGP origin AS change (OASC) events

Soon Tee Teoh, Kwan-Liu Ma, S. Felix Wu, Dan Massey, Xiao Liang Zhao, Dan Pei, Lan Wang, Lixia Zhang, Randy Bush

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


To complement machine intelligence in anomaly event analysis and correlation, in this paper, we investigate the possibility of a human-interactive visual-based anomaly detection system for faults and security attacks related to the BGP (Border Gateway Protocol) routing protocol. In particular, we have built and tested a program, based on fairly simple information visualization techniques, to navigate interactively real-life BGP OASC (Origin AS Change) events. Our initial experience demonstrates that the integration of mechanical analysis and human intelligence can effectively improve the performance of anomaly detection and alert correlation. Furthermore, while a traditional representation of OASC events provides either little or no valuable information, our program can accurately identify, correlate previously unknown BGP/OASC problems, and provide network operators with a valuable high-level abstraction about the dynamics of BGP.

Original languageEnglish (US)
Pages (from-to)155-168
Number of pages14
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - Dec 1 2003

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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