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A machine learning evaluation of an artificial immune system.
Evol Comput. 2005 Summer; 13(2):179-212.EC

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.

Authors+Show Affiliations

Department of Computer Science, University of New Mexico, Albuquerque, NM 87131-1386, USA. glickman@cs.unm.eduNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

15969900

Citation

Glickman, Matthew, et al. "A Machine Learning Evaluation of an Artificial Immune System." Evolutionary Computation, vol. 13, no. 2, 2005, pp. 179-212.
Glickman M, Balthrop J, Forrest S. A machine learning evaluation of an artificial immune system. Evol Comput. 2005;13(2):179-212.
Glickman, M., Balthrop, J., & Forrest, S. (2005). A machine learning evaluation of an artificial immune system. Evolutionary Computation, 13(2), 179-212.
Glickman M, Balthrop J, Forrest S. A Machine Learning Evaluation of an Artificial Immune System. Evol Comput. 2005;13(2):179-212. PubMed PMID: 15969900.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A machine learning evaluation of an artificial immune system. AU - Glickman,Matthew, AU - Balthrop,Justin, AU - Forrest,Stephanie, PY - 2005/6/23/pubmed PY - 2005/9/30/medline PY - 2005/6/23/entrez SP - 179 EP - 212 JF - Evolutionary computation JO - Evol Comput VL - 13 IS - 2 N2 - 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. SN - 1063-6560 UR - https://www.unboundmedicine.com/medline/citation/15969900/A_machine_learning_evaluation_of_an_artificial_immune_system_ L2 - https://direct.mit.edu/evco/article-lookup/doi/10.1162/1063656054088503 DB - PRIME DP - Unbound Medicine ER -