%0 Conference Proceedings
%T Hybrid Negative Selection Approach for Anomaly Detection
%+ Białystok University of Technology
%+ Gdańsk University of Technology (GUT)
%+ Polska Akademia Nauk = Polish Academy of Sciences = Académie polonaise des sciences (PAN)
%A Chmielewski, Andrzej
%A Wierzchoń, Sławomir, T.
%Z Part 5: Algorithms and Data Management
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 11th International Conference on Computer Information Systems and Industrial Management (CISIM)
%C Venice, Italy
%Y Agostino Cortesi
%Y Nabendu Chaki
%Y Khalid Saeed
%Y Sławomir Wierzchoń
%I Springer
%3 Computer Information Systems and Industrial Management
%V LNCS-7564
%P 242-253
%8 2012-09-26
%D 2012
%R 10.1007/978-3-642-33260-9_21
%K Artificial immune system
%K anomaly detection
%K multi-dimensional data
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X 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.
%G English
%Z TC 8
%2 https://inria.hal.science/hal-01551730/document
%2 https://inria.hal.science/hal-01551730/file/978-3-642-33260-9_21_Chapter.pdf
%L hal-01551730
%U https://inria.hal.science/hal-01551730
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC8
%~ IFIP-CISIM
%~ IFIP-LNCS-7564