A Negative Selection Algorithm Base on the Self R-Tree

Data Query ◽

Query Process ◽

Algorithm Base ◽

In this article, we present a new negative selection algorithm which the self-data is organized as a R-Tree structure. And the negative selection process could be transformed into the data query process in the self-R-Tree, if a new detector is indexed in any leaf node it will be dropped. As the time complexity of data query process in the tree is in the log level, the negative selection process of our algorithm is superior to the linearly comparation procedure in the traditional negative selection algorithms.

GF-NSA: A Negative Selection Algorithm Based on Self Grid File

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.44-47.3200 ◽

2010 ◽

Vol 44-47 ◽

pp. 3200-3203 ◽

Cited By ~ 1

Author(s):

Tao Yang ◽

Hong Li Deng ◽

Wen Chen ◽

Zhe Wang

Keyword(s):

Negative Selection ◽

Selection Process ◽

The Self ◽

Selection Algorithm ◽

Selection Efficiency ◽

Data Set ◽

Creation Process ◽

Data Query

The efficiency of traditional negative selection algorithm is too low to be applied. In our algorithm before the detector creation process, the structure of self data set is pre-treated to be a grid file, and the negative selection process is transformed into a data query procedure in the self grid file to improve the negative selection efficiency. Furthermore, each detector recorded its grid id to dynamically update itself along with the variations of self data in the grid.

A Real-Valued Negative Selection Algorithm Based on Grid for Anomaly Detection

Abstract and Applied Analysis ◽

10.1155/2013/268639 ◽

2013 ◽

Vol 2013 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Ruirui Zhang ◽

Tao Li ◽

Xin Xiao

Keyword(s):

Negative Selection ◽

Artificial Immune Systems ◽

Real Space ◽

Time Cost ◽

Selection Algorithm ◽

Theory Analysis ◽

Matching Process ◽

Self Tolerance ◽

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 then-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.

A Novel Negative Selection Algorithm with Optimal Worst-case Training Time Complexity for R-chunk Detectors

Indian Journal of Science and Technology ◽

10.17485/ijst/2020/v13i10/149803 ◽

2020 ◽

Vol 13 (10) ◽

pp. 1160-1171 ◽

Cited By ~ 1

Author(s):

Nguyen Van Truong

Keyword(s):

Time Complexity ◽

Negative Selection ◽

Selection Algorithm ◽

Worst Case ◽

Training Time ◽

Negative Selection Algorithm

A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.121-122.486 ◽

2010 ◽

Vol 121-122 ◽

pp. 486-489

Author(s):

Wen Chen ◽

Tao Li ◽

Xiao Jie Liu ◽

Yuan Quan Shi

Keyword(s):

Negative Selection ◽

The Self ◽

Hierarchical Level ◽

Selection Algorithm ◽

Artificial Immune ◽

Data Set ◽

Cluster Radius ◽

Value Range ◽

Level Cluster

In this article, we proposed a negative selection algorithm which based on hierarchical level cluster of self dataset CB-RNSA. First the self data set is clustered by different cluster radius, and then the self data are substituted by cluster centers to compare with candidate detectors to reduce the number of distance counting. In the detector creating process, the value of each detector property was restricted to a given value range so as to decrease the redundancy of detectors. The stimulation result shows that CB-RNSA is an effective algorithm for the creation of artificial immune detectors.

A Clone Selection Based Real-Valued Negative Selection Algorithm

Complexity ◽

10.1155/2018/2520940 ◽

2018 ◽

Vol 2018 ◽

pp. 1-20

Author(s):

Ruirui Zhang ◽

Xin Xiao

Keyword(s):

Negative Selection ◽

Immune Cell ◽

Clonal Selection ◽

Space Distribution ◽

Small Radius ◽

Clonal Selection Algorithm ◽

Selection Algorithm ◽

Immune Cell Population ◽

Excessive detectors, high time complexity, and loopholes are main problems which current negative selection algorithms have face and greatly limit the practical applications of negative selection algorithms. This paper proposes a real-valued negative selection algorithm based on clonal selection. Firstly, the algorithm analyzes the space distribution of the self set and gets the set of outlier selves and several classification clusters. Then, the algorithm considers centers of clusters as antigens, randomly generates initial immune cell population in the qualified range, and executes the clonal selection algorithm. Afterwards, the algorithm changes the limited range to continue the iteration until the non-self space coverage rate meets expectations. After the algorithm terminates, mature detector set and boundary self set are obtained. The main contributions lie in (1) introducing the clonal selection algorithm and randomly generating candidate detectors within the stratified limited ranges based on clustering centers of self set; generating big-radius candidate detectors first and making them cover space far from selves, which reduces the number of detectors; then generating small-radius candidate detectors and making them gradually cover boundary space between selves and non-selves, which reduces the number of holes; (2) distinguishing selves and dividing them into outlier selves, boundary selves, and internal selves, which can adapt to the interference of noise data from selves; (3) for anomaly detection, using mature detector set and boundary self set to test at the same time, which can effectively improve the detection rate and reduce the false alarm rate. Theoretical analysis and experimental results show that the algorithm has better time efficiency and detector generation quality according to classic negative selection algorithms.

A real negative selection algorithm with evolutionary preference for anomaly detection

Open Physics ◽

10.1515/phys-2017-0013 ◽

2017 ◽

Vol 15 (1) ◽

pp. 121-134 ◽

Cited By ~ 5

Author(s):

Tao Yang ◽

Wen Chen ◽

Tao Li

Keyword(s):

Negative Selection ◽

Feature Space ◽

Dimensional Subspace ◽

Experimental Result ◽

High Dimensional ◽

Selection Algorithm ◽

Preference Theory ◽

Low Dimensional ◽

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.