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26 septembre 2012
- handle: 10670/1.ckvt1i
- hal: hal-01551730
- doi: 10.1007/978-3-642-33260-9_21
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-642-33260-9_21
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
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Andrzej Chmielewski et al., « Hybrid Negative Selection Approach for Anomaly Detection », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1007/978-3-642-33260-9_21
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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.