A Change-Detection Method Inspired by the
Theory, Algorithms and Techniques
Department of Computer Science University of New Mexico Albuquerque, New Mexico, 87131 [email protected]
Technical Report No. CS95-6 November 1, 1995
This report gives an overview of the tools developed for the change-detection method based on the immune system, introduced by Forrest et al. in  and . Some general theoretical results are given. Consequences of the detector generating algorithms from  and  are examined and some preliminary results with these algorithms are analyzed. Finally, guidelines are given for choosing the various parameters of the matching rule when applying this method to a real data stream.
The change-detection method introduced in  and  distinguishes ?self? strings from ?nonself? strings by generating detectors for anything that is not in the self set. This is similar to the generation of T-cells in the immune system. In the thymus, T-cells with effectively random receptors are generated, but before they are released in the bloodstream those T-cells that match self proteins are deleted.
This principle of generating random detectors and then censoring the ones that match the self strings (according to a predefined matching rule) is used in  to construct a detector set that matches nonself strings with a given probability of success. More efficient detector generating algorithms are described in  and . However, these generate only valid detectors for a specific matching rule (r-contiguousbits matching). This report will focus on the theoretical background of this kind of change-detection method, as well as on the consequences of the algorithms described in [5, 6, 7, 8], and the issues involved in the practical application of these techniques.