Reflector attack traceback system with pushback based iTrace mechanism

Hyung Woo Lee, Sung Hyun Yun, Taekyoung Kwon, Jae Sung Kim, Hee Un Park, Nam Ho Oh

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

Reflector attack belongs to one of the most serious types of Distributed Denial-of-Service (DDoS) attacks, which can hardly be traced by traceback techniques, since the marked information written by any routers between the attacker and the reflectors will be lost in the replied packets from the reflectors. In response to such attacks, advanced IP traceback technology must be suggested. This study proposed an improved iTrace technique that identifies DDoS traffics with Pushback based multi-hop iTrace mechanism based on authenticated packet marking information at reflector for malicious reflector source trace and cope with DDoS attack packets efficiently.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsJavier Lopez, Sihan Qing, Eiji Okamoto
PublisherSpringer Verlag
Pages236-248
Number of pages13
ISBN (Print)3540235639, 9783540235637
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3269
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lee, H. W., Yun, S. H., Kwon, T., Kim, J. S., Park, H. U., & Oh, N. H. (2004). Reflector attack traceback system with pushback based iTrace mechanism. In J. Lopez, S. Qing, & E. Okamoto (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 236-248). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3269). Springer Verlag. https://doi.org/10.1007/978-3-540-30191-2_19