A machine learning evaluation of an artificial immune system |
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Authors: | Glickman Matthew Balthrop Justin Forrest Stephanie |
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Affiliation: | Department of Computer Science, University of New Mexico, Albuquerque, NM 87131-1386, USA. glickman@cs.unm.edu |
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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. |
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