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Improved thresholding based on negative selection algorithm (NSA)

EVOLUTIONARY INTELLIGENCE 2014年第3期6卷 157-170页

作者： Mahapatra, Prasant Kumar Kaur, Mandeep Sethi, Spardha Thareja, Rishabh Kumar, Amod Devi, Swapna CSIR Biomed Instrumentat Div V 2 Chandigarh 160030 India Panjab Univ Univ Inst Engn & Technol Chandigarh India Natl Inst Tech Teachers Training & Res Dept Elect & Commun Chandigarh 160026 India

Thresholding is a tool of image segmentation which groups the pixels in a logical way. In this paper, a novel algorithm based on negative selection algorithm a model of artificial immune system is proposed for image thresholding. The proposed algorithm is applied on the thresholded images of lathe tool produced using maximum information entropy (MIE) and global thresholding based technique resulting in an improved image. To verify the algorithm and results, it has also been applied on some of the inbuilt MATLAB (MATrix LABoratory) images. Histogram is employed to analyze the results. Further, the results of improved algorithm are compared with the results of MIE and the global thresholding methods to check the effectiveness of the proposed method. The experimental results confirm the potential of the developed algorithm.

关键词： Thresholding Image segmentation negative selection algorithm Artificial immune system Maximum information entropy

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A survey of intrusion detection techniques based on negative selection algorithm

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2022年第SUPPL 1期13卷 175-185页

作者： Singh, Kuldeep Kaur, Lakhwinder Maini, Raman Punjabi Univ Dept Comp Sci & Engn Patiala Punjab India

The Artificial Immune System (AIS) is a powerful information processing system in adaptability, distribution, self-regulation, and decentralization control in computer networks inspired by the human immune system (HIS). NSA is the primary method used in AIS. It is based on self and non-self-discrimination observed in the HIS. This work introduces the recent improvements in the NSA in the field of Intrusion detection in computer networks, wireless sensor networks (WSN), Internet of things (IoT) and aircraft system. The literature shows that most of the authors have used the NSL-KDD dataset to evaluate the performance of their proposed work, Euclidean distance as similarity measure, info gain and principal component analysis (PCA) as dimensionality reduction algorithm.

关键词： Artificial immune system Security negative selection algorithm Intrusion detection

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A malware detection model based on a negative selection algorithm with penalty factor

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Science China(Information Sciences) 2010年第12期53卷 2461-2471页

作者： ZHANG PengTao 1,2 , WANG Wei 1,2 & TAN Ying 1,2 1 Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China 2 Key Laboratory of Machine Perception, Ministry of Education, Peking University, Beijing 100871, China 1. Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing 100871 China2. Key Laboratory of Machine Perception Ministry of Education Peking University Beijing 100871 China

A malware detection model based on a negative selection algorithm with penalty factor (NSAPF) is proposed in this paper. This model extracts a malware instruction library (MIL), containing instructions that tend to appear in malware, through deep instruction analysis with respect to instruction frequency and file frequency. From the MIL, the proposed model creates a malware candidate signature library (MCSL) and a benign program malware-like signature library (BPMSL) by splitting programs orderly into various short bit strings. Depending on whether a signature matches "self", the NSAPF further divides the MCSL into two malware detection signature libraries (MDSL1 and MDSL2), and uses these as a two-dimensional reference for detecting suspicious programs. The model classifies suspicious programs as malware and benign programs by matching values of the suspicious programs with MDSL1 and MDSL2. Introduction of a penalty factor C in the negative selection algorithm enables this model to overcome the drawback of traditional negative selection algorithms in defining the harmfulness of "self" and "nonself", and focus on the harmfulness of the code, thus greatly improving the effectiveness of the model and also enabling the model to satisfy the different requirements of users in terms of true positive and false positive rates. Experimental results confirm that the proposed model achieves a better true positive rate on completely unknown malware and a better generalization ability while keeping a low false positive rate. The model can balance and adjust the true positive and false positive rates by adjusting the penalty factor C to achieve better performance.

关键词： penalty factor negative selection algorithm signature extraction artificial immune system mal- ware detection

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negative selection algorithm based methodology for online structural health monitoring

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ENGINEERING STRUCTURES 2021年 229卷 111662-111662页

作者： Barontini, Alberto Masciotta, Maria Giovanna Amado-Mendes, Paulo Ramos, Luis F. Lourenco, Paulo B. Univ Minho Guimaraes Dept Civil Engn ISISE Portugal Campus Azurem P-4800058 Guimaraes Portugal Univ G dAnnunzio Dept Engn & Geol Viale Pindaro 42 Pescara Italy Univ Coimbra ISISE Dept Civil Engn Coimbra Portugal R Luis Reis SantosPolo II P-3030788 Coimbra Portugal

Even small damages in engineered systems can endanger the users or lead to significant economic costs. The detection of damage existence is only the first task within the Damage Identification strategy, nonetheless, it is still very complex, especially for Civil Engineering products. The present study is devoted to the analysis of a global methodology for damage detection based on a recently developed version of the negative selection algorithm (NSA). Given any new monitored data measured from the system, the methodology addresses the one-class classification problem by labelling it as normal or abnormal, thus potentially damaged. The strategy is independent of the engineered system under investigation and of the damage-sensitive features used to assess the state, as long as only two features per time are analysed by the classifier. The methodology includes a set of features meant to tackle typical shortcomings and open issues which might prevent an effective application of NSA to damage detection. Some of them are shared with other soft computing techniques for classifications. Finally, the methodology is applied to the data collected from a scaled masonry arch built and tested at the University of Minho (Portugal). The datasets are used to validate the algorithm features, to compare the NSA with other well-known classification algorithms and to simulate a real monitoring to assess the readiness of the algorithm response.

关键词： negative selection algorithm Masonry Arch Damage Detection Structural Health Monitoring

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Research on a randomized real-valued negative selection algorithm

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Journal of Harbin Institute of Technology(New Series) 2006年第6期13卷 745-747页

作者：张凤斌王胜文郝忠孝 Dept. of Computer Science and Technology Harbin Institute of Technology Dept. of Computer Science and Technology Harbin University of Science and Technology Harbin 150080China Dept. of Computer Science and Technology Tsinghua University

A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance.

关键词： intrusion detection immune systems negative selection algorithm

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Intrusion Detection systems using Real-Valued negative selection algorithm with Optimized Detectors 5

Intrusion Detection systems using Real-Valued Negative Selec...

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5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)

作者： Selahshoor, Fatemeh Jazayeriy, Hamid Omranpour, Hesam Babol Noshirvani Univ Technol Elect & Comp Engn Dept Babol Iran

ISBN: (纸本)9781728153506

Intrusion detection system (IDS) is one of the key security mechanisms to preserve integrity against attacks in networks Immunity-base intrusion detection is a well-known approach to develop IDS in WSN. The negative selection algorithm (NSA) is one of the immunity-base methods for classification of network attacks. The selection of the proper radius for self-samples is a challenging problem in NSA. These radiuses are used for training and constructing detectors. The incorrect selection of a radius can cause the problem of boundaries invasion and the overlap between the samples, which increase the false detection rate. To solve this problem, self-samples must be optimized before being used in the detection phase. In this paper, we propose a variable radius for self-sampling based on affinity density to reduce the false detection. The NSL-KDD data set is used to evaluate the proposed method. The results indicate that the proposed method can reduce the false detection rate and along with increasing true positive rate.

关键词： Intrusion Detection System negative selection algorithm Artficial Immune System Network

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Applying the negative selection algorithm for Merger and Acquisition Target Identification Theory and Case Study

Applying the Negative Selection Algorithm for Merger and Acq...

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1st BRICS Countries Congress on Computational Intelligence / 11th Brazilian Congress on Computational Intelligence (BRICS-CCI and CBIC)

作者： Paul, Satyakama Janacek, Andreas de Lima Neto, Fernando Buarque Marwala, Tshilidzi Univ Johannesburg Dept Mech Engn Sci FEBE APK Campus ZA-2006 Johannesburg South Africa Univ Johannesburg RTI ZA-2006 Johannesburg South Africa Univ Vienna Res Grp Theory & Applicat Algorithms A-1090 Vienna Austria Univ Permambuco POLI Comp Eng Program BR-720001 Recife PE Brazil

ISBN: (纸本)9781479931941

In this paper, we propose a new methodology based on the negative selection algorithm that belongs to the field of Computational Intelligence (specifically, Artificial Immune Systems - AIS) to identify takeover targets. Although considerable research based on customary statistical techniques and some contemporary Computational Intelligence techniques have been devoted to identify takeover targets, most of the existing studies are based upon multiple previous mergers and acquisitions. Contrary to previous research, the novelty of this proposal lies in the methodology's ability to suggest takeover targets for novice firms that are at the beginning of their merger and acquisition spree. We first discuss the theoretical perspective and then provide a case study with details for practical implementation, both capitalizing from unique generalization capabilities of AIS algorithms.

关键词： Merger and Acquisition M&A Takeover target prediction Artificial Immune System negative selection algorithm Euclidean distance Cosine similarity

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Quantum-negative selection algorithm for Associative Classification

Quantum-Negative Selection Algorithm for Associative Classif...

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IEEE International Conference on Granular Computing (GrC)

作者： Soliman, Omar S. Adly, Amr Cairo Univ Fac Comp & Informat Cairo Egypt

ISBN: (纸本)9781467323109;9781467323116

Most of classification and rule learning algorithms in machine learning use heuristic search to find part of rules for classification. Classification Associative Classification (AC) has shown a great dominance over many classification techniques. It integrates the rule discovery and classification process to build the classifier that supports decision making process. Artificial Immune Systems (AIS) have emerged during the last decade, It uses the population-based search model of evolutionary computation algorithms that it is regarded as a suitable way for dealing with complex search space. This paper proposes a Quantum-negative selection algorithm (Q-NSA) for associative classification. It integrates quantum computing concepts and negative selection algorithm (NSA) to building an efficient classifier by generating rule detectors to find the best subset of rules for all possible association rules. It employees a mutation operator with a quantum-based rotation gate to control and maintain diversity, and guides the search process. The performance of proposed algorithm is evaluated using benchmark datasets. The experimental results showed that the proposed algorithm is preformed well with large search space and has higher accuracy, and maintain diversity.

关键词： Associative Classification Quantum Computing Artificial Immune Systems (AIS) negative selection algorithm Q-gate mutation Pruning Process

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Optimization and Application of Real-valued negative selection algorithm

Optimization and Application of Real-valued Negative Selecti...

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International Conference on Power Electronics and Engineering Application (PEEA)

作者： Xu Aiqiang Liu Yong Zhao Xiuli Yang Chunying Li Tingjun Naval Aeronaut & Astronaut Univ Yantai 264001 Peoples R China

Concerned with the problem of lacking fault samples of complex equipments, it studies the principle and application of negative selection algorithm of artificial immune system. The detectors generation mechanism of real-valued negative selection algorithm is introduced. Intuitively, to maximize the covering produced by a set of detectors, it is necessary to reduce their overlapping and not covering the self set. This paper presents an optimization strategy base on re-heating simulated annealing algorithm to modify the position of detectors, not changing their number. This method can improve the covering effect of non-self space. The triangle training data are used to demonstrate the properties of optimized VRNS. Detection rate is improved and false alarming rate is decreased. It is used for fault detection in analog circuit;result demonstrates that the proposed algorithm is better than artificial neural network in fault detection of this circuit. (C) 2011 Published by Elsevier Ltd. selection and/or peer-review under responsibility of [name organizer]

关键词： negative selection algorithm re-heating simulated annealing optimization fault detection

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Fault Diagnosis of Induction Motor Based on wavelet packet analysis and negative selection algorithm

Fault Diagnosis of Induction Motor Based on wavelet packet a...

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International Conference on Manufacturing Engineering and Automation (ICMEA 2012)

作者： Yuan, Gui Li Qin, Shi Wei North China Elect Power Univ Baoding Peoples R China

ISBN: (纸本)9783037855300

For the spectrum characteristics of the motor vibration, the vibration fault diagnosis method of motor rotor rubbing based on wavelet packet transform and real-valued negative selection algorithm is put forward. Using wavelet packet analysis to energy analysis of rotor rubbing and extracting the fault feature vectors, then by real-valued negative selection algorithm to identify the normal and failure mode eigenvectors. The experimental results show that with this method all the rotor rubbing faults can be detected comprehensive and rapidly. This method has high feasibility of the wavelet packet analysis and real-valued negative selection algorithm in the rotor rubbing fault diagnosis.

关键词： Wavelet packet analysis negative selection algorithm Fault diagnosis Rotor rubbing

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