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.

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.

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 Design of Fixed and Ladder Mutation Factor-Based Clonal Selection Algorithm for Solving Unimodal and Multimodal Functions

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Suresh Chittineni ◽  
A. N. S. Pradeep ◽  
Dinesh Godavarthi ◽  
Suresh Chandra Satapathy ◽  
S. Mohan Krishna ◽  
...  
Keyword(s):  
Clonal Selection ◽  
Constant Factor ◽  
Clonal Selection Algorithm ◽  
Solution Stability ◽  
Selection Algorithm ◽  
Human Immune System ◽  
Solution Convergence ◽  
New Concepts ◽  
Clonal Selection Algorithms ◽  
Selection Algorithms

Clonal selection algorithms (CSAs) is a special class of immune algorithms (IA), inspired by the clonal selection principle of the human immune system. To improve the algorithm's ability to perform better, this CSA has been modified by implementing two new concepts called fixed mutation factor and ladder mutation factor. Fixed mutation factor maintains a constant factor throughout the process, where as ladder mutation factor changes adaptively based on the affinity of antibodies. This paper compared the conventional CLONALG, with the two proposed approaches and tested on several standard benchmark functions. Experimental results empirically show that the proposed methods ladder mutation-based clonal selection algorithm (LMCSA) and fixed mutation clonal selection algorithm (FMCSA) significantly outperform the existing CLONALG method in terms of quality of the solution, convergence speed, and solution stability.

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.

A Novel Approach of Detector Generation for Real-Valued Negative Selection Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 3736-3740
Author(s):  
Rong Hua Hu ◽  
Pei Huang Lou ◽  
Peng Zhao
Keyword(s):  
Negative Selection ◽  
Clonal Selection ◽  
Dimensional Space ◽  
Random Sequence ◽  
Neighborhood Search ◽  
Selection Algorithm ◽  
Negative Selection Algorithm ◽  
Novel Approach ◽  
Gaussian Mutation Operator ◽  
Good Coverage

The detector sets generated by Real-Valued Negative Selection Algorithm (RNSA) are usually numerous, without optimization, and can not work under real-time condition. Thus, a novel approach of detector generation for RNSA based on Clonal Selection and Neighborhood Search (CSNS-RNSA) is proposed. Clonal selection of the immune mechanism is introduced to implement global search in a quasi-random sequence. The Gaussian mutation operator is proposed to get the global optimal detection sets of N-dimensional space through Neighborhood search. The resulting detector sets achieved a good coverage of non-self space, and also significantly reduced the number of detector sets, thus overcome the limitations of original RNSA. Finally, experiments verify the effectiveness of the algorithm.

scholarly journals Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Ramsha Rizwan ◽  
Farrukh Aslam Khan ◽  
Haider Abbas ◽  
Sajjad Hussain Chauhdary
Keyword(s):  
Clonal Selection ◽  
Wireless Sensor ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Biologically Inspired ◽  
Data Packets ◽  
Negative Selection Algorithm ◽  
Bee Colony ◽  
Anomalous Data ◽  
Network Intrusion

During the past few years, we have seen a tremendous increase in various kinds of anomalies in Wireless Sensor Network (WSN) communication. Recently, researchers have shown a lot of interest in applying biologically inspired systems for solving network intrusion detection problems. Several solutions have been proposed using Artificial Immune System (AIS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and so forth. In this paper, we propose a bioinspired solution using Negative Selection Algorithm (NSA) of the AIS for anomalies detection in WSNs. For this purpose, we implement the enhanced NSA and make a detector set that holds anomalous packets only. Then the random packets are tested and matched with the detector set and anomalies are identified. Anomalous data packets are used for further processing to identify specific anomalies. In this way, the number of wormholes, packets delayed, and packets dropped are calculated and identified. Simulations are performed on a large dataset and the results show high accuracy of the proposed algorithm in detecting anomalies. The proposed NSA is also compared with Clonal Selection Algorithm (CSA) for the same dataset. The results show significant improvement of the proposed NSA over CSA in most of the cases.

Immune-Based Fault Diagnosis Method for Gas Valve

2006 ◽  
Author(s):  
Jiye Shao ◽  
Rixin Wang ◽  
Jinbo Gao ◽  
Minqiang Xu
Keyword(s):  
Fault Diagnosis ◽  
Negative Selection ◽  
Clonal Selection ◽  
Vibration Signal ◽  
Frequency Interval ◽  
Reciprocating Compressor ◽  
Selection Algorithm ◽  
Selection Principle ◽  
Negative Selection Algorithm ◽  
Diagnosis Method

Negative selection principle based on the natural immune system makes it possible to develop a new fault diagnosis technique. This paper investigates a fault diagnosis method for the gas valve of the reciprocating compressor based on the negative selection algorithm. The negative selection algorithm combining the clonal selection principle with the mutation theory is used to produce the detectors representing one fault by the sample indicating normal. The detectors representing abnormal are used to perform the fault detection and recognition. The preprocessing using discrete fourier transform to extract the characteristic frequency interval makes the method more efficient and reliable. Experimental data of three kinds of fault from the vibration signal of the gas valve of the reciprocating compressor is used to test the method. The results prove that this method is very efficient.

scholarly journals A Novel Immune-Inspired Shellcode Detection Algorithm Based on Hyperellipsoid Detectors

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Tianliang Lu ◽  
Lu Zhang ◽  
Yixian Fu
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Clonal Selection ◽  
Detection System ◽  
Detection Algorithm ◽  
Machine Language ◽  
Detection Accuracy ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Negative Selection Algorithm

Shellcodes are machine language codes injected into target programs in the form of network packets or malformed files. Shellcodes can trigger buffer overflow vulnerability and execute malicious instructions. Signature matching technology used by antivirus software or intrusion detection system has low detection rate for unknown or polymorphic shellcodes; to solve such problem, an immune-inspired shellcode detection algorithm was proposed, named ISDA. Static analysis and dynamic analysis were both applied. The shellcodes were disassembled to assembly instructions during static analysis and, for dynamic analysis, the API function sequences of shellcodes were obtained by simulation execution to get the behavioral features of polymorphic shellcodes. The extracted features of shellcodes were encoded to antigens based on n-gram model. Immature detectors become mature after immune tolerance based on negative selection algorithm. To improve nonself space coverage rate, the immune detectors were encoded to hyperellipsoids. To generate better antibody offspring, the detectors were optimized through clonal selection algorithm with genetic mutation. Finally, shellcode samples were collected and tested, and result shows that the proposed method has higher detection accuracy for both nonencoded and polymorphic shellcodes.

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