Abstract
The natural immune system has evolved many interesting mechanisms to solve the problem of self-nonself discrimination. An anomaly detection system based upon principles derived from the immune system was introduced in [Forr94]. Its main advantages are that it is distributable, local, and tunable. This paper provides an overview of the theoretical, algorithmic,and practical developments extending the original proposal. In particular, we present information theoretic results on the detection method, show the possibility of strings that cannot be detected for a given combination of self set and matching rule, present efficient algorithms to generate the detector set, and provide rules of thumb for setting the parameters to apply this method to a real data set.
