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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Hybrid Negative Selection Approach for Anomaly Detection
verfasst von : Andrzej Chmielewski, Sławomir T. Wierzchoń
Erschienen in: Computer Information Systems and Industrial Management
Verlag: Springer Berlin Heidelberg
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This paper describes a
b
-
v
model which is enhanced version of the negative selection algorithm (
NSA
). In contrast to formerly developed approaches, binary and real-valued detectors are simultaneously used. The reason behind developing this hybrid is our willingness to overcome the scalability problems occuring when only one type of detectors is used. High-dimensional datasets are a great challenge for
NSA
. But the quality of generated detectors, duration of learning stage as well as duration of classification stage need a careful treatment also. Thus, we discuss various versions of the
b
-
v
model developed to increase its efficiency. Versatility of proposed approach was intensively tested by using popular testbeds concerning domains like computer’s security (intruders and spam detection) and recognition of handwritten words.