A Real-Valued Negative Selection Algorithm Based on Grid for Anomaly Detection
Ruirui Zhang,Tao Li,Xin Xiao +2 more
TL;DR: GB-RNSA lowers the number of detectors, time complexity, and false alarm rate, and certain methods are adopted to reduce duplication coverage between detectors, which achieves fewer detectors covering the nonself space as much as possible.
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Abstract: Negative selection algorithm is one of the main algorithms of artificial immune systems. However, candidate detectors randomly generated by traditional negative selection algorithms need to conduct self-tolerance with all selves in the training set in order to eliminate the immunological reaction. The matching process is the main time cost, which results in low generation efficiencies of detectors and application limitations of immune algorithms. A novel algorithm is proposed, named GB-RNSA. The algorithm analyzes distributions of the self set in real space and regards the n-dimensional [0, 1] space as the biggest grid. Then the biggest grid is divided into a finite number of sub grids, and selves are filled in the corresponding subgrids at the meantime. The randomly generated candidate detector only needs to match selves who are in the grid where the detector is and in its neighbor grids, instead of all selves, which reduces the time cost of distance calculations. And before adding the candidate detector into mature detector set, certain methods are adopted to reduce duplication coverage between detectors, which achieves fewer detectors covering the nonself space as much as possible. Theory analysis and experimental results demonstrate that GB-RNSA lowers the number of detectors, time complexity, and false alarm rate.
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References
Immune anomaly detection enhanced with evolutionary paradigms
Marek Ostaszewski,Franciszek Seredynski,Pascal Bouvry +2 more
- 08 Jul 2006
TL;DR: Results of experiments show a high quality of intrusion detection, which outperform the quality of recently proposed approach based on hypersphere representation of self-space.
29
Clonal Optimization of Negative Selection Algorithm with Applications in Motor Fault Detection
Xiao-Zhi Gao,Seppo J. Ovaska,Xiaolei Wang,Mo-Yuen Chow +3 more
- 01 Oct 2006
TL;DR: Taking advantage of the clonal optimization strategy, the NSA detectors can be optimized for anomaly detection and a new motor fault detection scheme using the authors' NSA is discussed.
21
Dynamic detection for computer virus based on immune system
TL;DR: The theory analysis and experimental results show that the proposed model has better time efficiency and detecting ability than the classic model ARTIS, and the difficult problem, in which the detector training cost is exponentially related to the size of self-set in a traditional computer immune system, is overcome.
The effect of binary matching rules in negative selection
Fabio A. González,Dipankar Dasgupta,Jonatan Piedra Gomez +2 more
- 12 Jul 2003
TL;DR: The purpose of the paper is to show that the low-level representation of binary matching rules is unable to capture the structure of some problem spaces.
•Book
The clonal selection theory of acquired immunity
F. M. Burnet
- 01 Jan 1959
TL;DR: The clonal selection theory of acquired immunity is studied as a theory of selection for immunity in the context of infectious disease.