Hybrid Negative Selection Approach for Anomaly Detection - Computer Information Systems and Industrial Management Access content directly
Conference Papers Year : 2012

Hybrid Negative Selection Approach for Anomaly Detection

Abstract

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.
Fichier principal
Vignette du fichier
978-3-642-33260-9_21_Chapter.pdf (398.33 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01551730 , version 1 (30-06-2017)

Licence

Attribution

Identifiers

Cite

Andrzej Chmielewski, Sławomir T. Wierzchoń. Hybrid Negative Selection Approach for Anomaly Detection. 11th International Conference on Computer Information Systems and Industrial Management (CISIM), Sep 2012, Venice, Italy. pp.242-253, ⟨10.1007/978-3-642-33260-9_21⟩. ⟨hal-01551730⟩
215 View
90 Download

Altmetric

Share

Gmail Facebook X LinkedIn More