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Fig. 1. Visualized concept of varying representations by means of permutation masks
to reduce the number of undetectable elements. The light gray shaded area in the
middle represents the self regions (normal class in terms of anomaly detection). The
dark gray shaded shapes represent areas which are covered by detectors with varying
representations. The white area represents the non-self space (anomalous class in terms
of anomaly detection). This figure is taken from [9].
In the following two sections we briefly introduce the standard negative selec-
tion inspired anomaly detection technique.
2
Artificial Immune System
An artificial immune system (AIS) [10] is a paradigm inspired by the immune
system and are used for solving computational and information processing prob-
lems. An AIS can be described, and developed, using a framework [10] which
contains the following basic elements:
- A representation for the artificial immune elements.
- A set of functions, which quantifies the interactions of the artificial immune
elements (anity).
- A set of algorithms which based on observed immune principles and methods.
This 3-step abstraction (representation, anity, algorithm) for using the AIS
framework is discussed in the following sections.
2.1
Hamming Shape-Space
The notion of shape-space was introduced by Perelson and Oster [11] and allows
a quantitative anity description between immune components known as an-
tibodies and antigens. More precisely, a shape-space is a metric space with an
associated distance (anity) function.
 
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