title: A machine learning evaluation of an artificial immune system.
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.affiliation: Department of Computer Science, University of New Mexico, Albuquerque, NM 87131-1386, USA. glickman@cs.unm.edu
authors.forename: Matthew
authors.initials: M
authors.lastname: Glickman
authors.forename: Justin
authors.initials: J
authors.lastname: Balthrop
authors.forename: Stephanie
authors.initials: S
authors.lastname: Forrest
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007107:Immune System; D018448:Models, Immunological; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012984:Software