Hybrid Negative Selection Approach for Anomaly Detection
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 proble...
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Published in | Computer Information Systems and Industrial Management pp. 242 - 253 |
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Main Authors | , |
Format | Book Chapter |
Language | English |
Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISBN: | 9783642332593 3642332595 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-642-33260-9_21 |