Quarterly (spring, summer, fall, winter)
176 pp. per issue
7 x 10
ISSN
1063-6560
E-ISSN
1530-9304
2014 Impact factor:
2.37

Evolutionary Computation

Summer 2005, Vol. 13, No. 2, Pages 179-212
(doi: 10.1162/1063656054088503)
© 2005 Massachusetts Institute of Technology
A Machine Learning Evaluation of an Artificial Immune System
Article PDF (294.69 KB)
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.