A Machine Learning Evaluation of an Artificial Immune System

Authors:
Matthew Glickman;Justin Balthrop;Stephanie Forrest
Affiliations:
Department of Computer Science, University of New Mexico, Albuquerque, NM 87131-1386, USA;Department of Computer Science, University of New Mexico, Albuquerque, NM 87131-1386, USA;Department of Computer Science, University of New Mexico, Albuquerque, NM 87131-1386, USA and Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
Venue:
Evolutionary Computation
Year:
2005

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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.