Research on the Network Fault Isolation Based on Negative Selection

2014 ◽  
Vol 926-930 ◽  
pp. 2079-2083
Author(s):  
Yan Guang Li
Keyword(s):  
Objective Function ◽  
Negative Selection ◽  
Fault Isolation ◽  
Experimental Result ◽  
Selection Algorithm ◽  
Negative Selection Algorithm ◽  
Network Fault ◽  
Fault Information ◽  
Operation Cost

An algorithm for constrained flooding based on negative selection is proposed due to improve delay of flooding and quality of operation when the network transmit the fault information. According to the network environment different demand between delay of flooding and quality of operation, cost function of flooding delay and operation losing are set, the optimizing objective function synthesize two factor is also set, and negative selection algorithm is used to compute the area of constrained flooding. The experimental result shows that this algorithm can improve the capability in network.

scholarly journals A real negative selection algorithm with evolutionary preference for anomaly detection

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 121-134 ◽  
Author(s):  
Tao Yang ◽  
Wen Chen ◽  
Tao Li
Keyword(s):  
Negative Selection ◽  
Feature Space ◽  
Dimensional Subspace ◽  
Experimental Result ◽  
High Dimensional ◽  
Selection Algorithm ◽  
Negative Selection Algorithm ◽  
Preference Theory ◽  
Low Dimensional ◽  
Selection Algorithms

AbstractTraditional real negative selection algorithms (RNSAs) adopt the estimated coverage (c0) as the algorithm termination threshold, and generate detectors randomly. With increasing dimensions, the data samples could reside in the low-dimensional subspace, so that the traditional detectors cannot effectively distinguish these samples. Furthermore, in high-dimensional feature space,c0cannot exactly reflect the detectors set coverage rate for the nonself space, and it could lead the algorithm to be terminated unexpectedly when the number of detectors is insufficient. These shortcomings make the traditional RNSAs to perform poorly in high-dimensional feature space. Based upon “evolutionary preference” theory in immunology, this paper presents a real negative selection algorithm with evolutionary preference (RNSAP). RNSAP utilizes the “unknown nonself space”, “low-dimensional target subspace” and “known nonself feature” as the evolutionary preference to guide the generation of detectors, thus ensuring the detectors can cover the nonself space more effectively. Besides, RNSAP uses redundancy to replacec0as the termination threshold, in this way RNSAP can generate adequate detectors under a proper convergence rate. The theoretical analysis and experimental result demonstrate that, compared to the classical RNSA (V-detector), RNSAP can achieve a higher detection rate, but with less detectors and computing cost.

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