Viterbi algorithm for intrusion type identification in anomaly detection system

Ja Min Koo, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Due to the proliferation of the infrastructure of communication networks and the development of the relevant technology, intrusions on computer systems and damage are increased, resulting in extensive work on intrusion detection systems (IDS) to find attacks exploiting illegal usages or misuses. However, many IDSs have some weaknesses, and most hackers try to intrude systems through the vulnerabilities. In this paper, we develop an intrusion detection system based on anomaly detection with hidden Markov model and propose a method using the Viterbi algorithm for identifying the type of intrusions. Experimental results indicate that the buffer overflow is well-identified, while we have some difficulties to identify the denial of service attacks with the proposed method.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsKijoon Chae, Moti Yung
PublisherSpringer Verlag
Pages97-110
Number of pages14
ISBN (Print)3540208275
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2908
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

    Koo, J. M., & Cho, S. B. (2004). Viterbi algorithm for intrusion type identification in anomaly detection system. In K. Chae, & M. Yung (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 97-110). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2908). Springer Verlag. https://doi.org/10.1007/978-3-540-24591-9_8