EvoSeedRNSAII: An improved evolutionary algorithm for generating detectors in the real-valued Negative Selection Algorithms

2014 ◽  
Vol 19 ◽  
pp. 18-30 ◽  
Author(s):  
Jie Zhang ◽  
Wenjian Luo
Keyword(s):  
Evolutionary Algorithm ◽  
Negative Selection ◽  
The Real ◽  
Selection Algorithms

scholarly journals Intrusion Detection in Wireless Sensor Networks with an Improved NSA Based on Space Division

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Ruirui Zhang ◽  
Xin Xiao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Negative Selection ◽  
Wireless Sensor ◽  
Selection Algorithm ◽  
The Real ◽  
Negative Selection Algorithm ◽  
Detection Process ◽  
Spatial Partition

Inspired by the biological immune system, many researchers apply artificial immune principles to intrusion detection in wireless sensor networks, such as negative selection algorithms, danger theory, and dendritic cell algorithms. When applying the negative selection algorithm to wireless sensor networks, the characteristics of wireless sensor networks, such as frequent changes in network topology and limited resources, are not considered too much, which makes the detection effect to need improvement. In this paper, a negative selection algorithm based on spatial partition is proposed and applied to hierarchical wireless sensor networks. The algorithm first analyzes the distribution of self-set in the real-valued space then divides the real-valued space, and several subspaces are obtained. Selves are filled into different subspaces. We implement the negative selection algorithm in the subspace. The randomly generated candidate detector only needs to be tolerated with selves in the subspace where the detector is located, not all the selves. This operation reduces the time cost of distance calculation. In the detection process of detectors, the antigen which is to be detected only needs to match the mature detectors in the subspace where the antigen is located, rather than all the detectors. This operation speeds up the antigen detection process. Theoretical analysis and experimental results show that the algorithm has better time efficiency and quality of detectors, saves sensor node resources and reduces the energy consumption, and is an effective algorithm for wireless sensor network intrusion detection.

A Negative Selection Algorithm Base on the Self R-Tree

2013 ◽  
Vol 411-414 ◽  
pp. 2007-2012
Author(s):  
Kun Peng Wang
Keyword(s):  
Time Complexity ◽  
Negative Selection ◽  
Selection Process ◽  
The Self ◽  
Selection Algorithm ◽  
Negative Selection Algorithm ◽  
Data Query ◽  
Query Process ◽  
Algorithm Base ◽  
Selection Algorithms

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.

A Study on the Performances of Danger Theory and Negative Selection Algorithms for Mobile Spam Detection

2017 ◽  
Vol 23 (5) ◽  
pp. 4586-4590 ◽  
Author(s):  
Nurul Fadhilah Sulaiman ◽  
Mohd Zalisham Jali ◽  
Zul Hilmi Abdullah ◽  
Shaharudin Ismail
Keyword(s):  
Negative Selection ◽  
Spam Detection ◽  
Danger Theory ◽  
Selection Algorithms

Immune K-means and negative selection algorithms for data analysis

2009 ◽  
Vol 179 (10) ◽  
pp. 1407-1425 ◽  
Author(s):  
Michał Bereta ◽  
Tadeusz Burczyński
Keyword(s):  
Data Analysis ◽  
Negative Selection ◽  
Selection Algorithms

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

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Ruirui Zhang ◽  
Tao Li ◽  
Xin Xiao
Keyword(s):  
Negative Selection ◽  
Artificial Immune Systems ◽  
Real Space ◽  
Time Cost ◽  
Selection Algorithm ◽  
Theory Analysis ◽  
Negative Selection Algorithm ◽  
Matching Process ◽  
Self Tolerance ◽  
Selection Algorithms

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.

scholarly journals A Clone Selection Based Real-Valued Negative Selection Algorithm

Complexity ◽  
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 ◽  
Negative Selection Algorithm ◽  
Selection Algorithms

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.

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