A machine learning evaluation of an artificial immune system

Matthew Glickman, Justin Balthrop, Stephanie Forrest

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

ARTIS is an artificial immune system framework which contains several adaptive mechanisms. LISYS is a version of ARTIS specialized for the problem of network intrusion detection. The adaptive mechanisms of LISYS are characterized in terms of their machine-learning counterparts, and a series of experiments is described, each of which isolates a different mechanism of LISYS and studies its contribution to the system's overall performance. The experiments were conducted on a new data set, which is more recent and realistic than earlier data sets. The network intrusion detection problem is challenging because it requires one-class learning in an on-line setting with concept drift. The experiments confirm earlier experimental results with LISYS, and they study in detail how LISYS achieves success on the new data set.

Original languageEnglish (US)
Pages (from-to)179-212
Number of pages34
JournalEvolutionary computation
Volume13
Issue number2
DOIs
StatePublished - Jun 2005
Externally publishedYes

Keywords

  • Anomaly detection
  • Artificial immune systems
  • Computer security
  • Immune system
  • Machine learning
  • Network intrusion detection

ASJC Scopus subject areas

  • Computational Mathematics

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