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An improved real-valued negative selection algorithm based on the constant detector for anomaly detection

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第5期40卷 8793-8806页

作者： Li, Dong Sun, Xin Gao, Furong Liu, Shulin Changzhou Univ Sch Petr Engn Changzhou Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Hong Kong Peoples R China

Compared with the traditional negative selection algorithms produce detectors randomly in whole state space, the boundary-fixed negative selection algorithm (FB-NSA) non-randomly produces a layer of detectors closely surrounding the self space. However, the false alarm rate of FB-NSA is higher than many anomaly detection methods. Its detection rate is very low when normal data close to the boundary of state space. This paper proposed an improved FB-NSA (IFB-NSA) to solve these problems. IFB-NSA enlarges the state space and adds auxiliary detectors in appropriate places to improve the detection rate, and uses variable-sized training samples to reduce the false alarm rate. We present experiments on synthetic datasets and the UCI Iris dataset to demonstrate the effectiveness of this approach. The results show that IFB-NSA outperforms FB-NSA and the other anomaly detection methods in most of the cases.

关键词： negative selection algorithm anomaly detection artificial immune algorithms machine learning

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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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negative selection algorithm Based Intrusion Detection Model 20

Negative Selection Algorithm Based Intrusion Detection Model

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20th IEEE Mediterranean Eletrotechnical Conference (IEEE MELECON)

作者： Tosin, Salau-Ibrahim Taofeekat Gbenga, Jimoh Rasheed Al Hikmah Univ Dept Comp Sci Ilorin Nigeria Univ Ilorin Fac Commun & Informat Sci Ilorin Nigeria

ISBN: (纸本)9781728152004

The ever-growing security challenges have been a hindrance to the success of Information Technology Innovations due to multifaceted network intrusions. Hence, it becomes imperative to provide tools that can address without compromising integrity, confidentiality and availability of network resources. This paper presents a model for detecting intrusion in a network using negative selection algorithm. negative selection which is Human Immune System (HIS) inspired has been used for anomaly detection due to its self-non-self-discrimination potential. However, it suffers from high rate of false positives and scalability issues. This paper addresses the issues using feature selection to reduce the dimensionality of the dataset. The intrusion detection model is evaluated using NSL-KDD dataset. The results obtained using the benchmark dataset showed that the scalability issue reduced in the proposed approach.

关键词： Intrusion Detection System Artificial Immune System negative selection algorithm Feature selection

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Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine

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COMPUTATIONAL INTELLIGENCE 2020年第3期36卷 1374-1393页

作者： Mohapatra, Santoshinee Khilar, Pabitra Mohan Natl Inst Technol Rourkela Dept Comp Sci & Engn Rourkela 769008 Odisha India

In this article, an improved negative selection algorithm (INSA) has been proposed to identify faulty sensor nodes in wireless sensor network (WSN) and then the faults are classified into soft permanent, soft intermittent, and soft transient fault using the support vector machine technique. The performance metrics such as fault detection accuracy, false alarm rate, false positive rate, diagnosis latency (DL), energy consumption, fault classification accuracy (FCA), and false classification rate (FCR) are used to evaluate the performance of the proposed INSA. The simulation result shows that the INSA gives better result as compared to the existing algorithms in terms of performance metrics. The fault classification performance is measured by FCA and FCR. It has also seen that the proposed algorithm gives less DL and consumes less energy than that of existing algorithms proposed by Mohapatra et al, Zhang et al, and Panda et al for WSN.

关键词： artificial immune system fault diagnosis negative selection algorithm support vector machine wireless sensor networks

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HD-NSA: A real-valued negative selection algorithm based on hierarchy division

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APPLIED SOFT COMPUTING 2021年 112卷 107726-107726页

作者： He, Junjiang Chen, Wen Li, Tao Li, Beibei Zhu, Yongbin Huang, Meng Sichuan Univ Coll Cybersecur 24 South Sect 1Yihuan Rd Chengdu Peoples R China

The negative selection algorithm (NSA) is an important algorithm for generating immune detectors in artificial immune systems. However, the original NSA randomly generates candidate detectors that produce a large number of redundant detectors, and it is difficult to cover the entire antibody space. Moreover, the randomly generated candidate detectors have to be compared with all the self-sets;therefore, the inefficient generation of the detector seriously influences the application of NSA. To overcome these defects, a real-valued NSA based on hierarchy division (HD-NSA) is proposed. First, the feature space is divided into self and non-self subgrids, and the center point of the non-self subgrid is specified as the candidate detector, and the specified candidate detector is compared with the self-antigens located in adjacent subgrids rather than with all the self-sets. Theoretical analysis demonstrated that the HD-NSA can effectively reduce the time complexity of the NSA algorithm. Furthermore, experiments on the Abalone data set show that the detector training time of HD-NSA decreased by 97.9%, 71.2%, 56.9% and 90.1%, respectively, compared with the classical RNSA, V-Detector, GF-RNSA and BIORV-NSA, whereas the detector detection rate increased by 50%, 25.8%, 13.8% and 10.5%, respectively. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Artificial immune system negative selection algorithm Hierarchical division Specified candidate detector Hole repair

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A hybrid real-valued negative selection algorithm with variable-sized detectors and the k-nearest neighbors algorithm

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KNOWLEDGE-BASED SYSTEMS 2021年 232卷 1页

作者： Li, Zhiyong Li, Tao He, Junjiang Zhu, Yongbin Wang, Yunpeng Sichuan Univ Sch Cyber Sci & Engn Chengdu 610065 Peoples R China Honghe Univ Informat Technol Ctr Mengzi 661199 Yunnan Peoples R China

A negative selection algorithm generates detectors to realize abnormality detection by simulating the maturation process of T cells in human immunity. Holes are areas of feature space that cannot be covered by the detector set and are the major factor in the degradation of algorithm performance. Conventional methods alleviate the hole problem by minimizing the coverage area of the holes. In this study, we approach the issue from a different angle. Holes are prone to form in the boundary area between the self and nonself regions, and when the self and the nonself cross or overlap, the hole problem becomes more serious. The k-nearest neighbors (k-NN) algorithm is more suitable than other methods for pending instance sets where the class domain crosses or overlaps more. Therefore, we propose a hybrid real-valued negative selection algorithm with variable-sized detectors (V-Detector) and the k-NN algorithm, abbreviated as V-Detector-kNN. The V-Detector-kNN hybrid algorithm first uses the V-Detector algorithm to classify, and then, for the problem that the nonself instances in the holes are misclassified as selfs, k-NN is introduced to classify those misclassified instances to improve the detection rate. Theoretical analysis proves that the V-Detector-kNN algorithm that we proposed has a higher detection rate than the V-Detector algorithm in most cases. Comparative experiments with 5 different algorithms on 9 UCI datasets show that our proposed algorithm ranks first in detection rate. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Artificial immune system negative selection algorithm V-Detector k-nearest neighbors Anomaly detection

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A negative selection algorithm based on adaptive immunoregulation 5

A negative selection algorithm based on adaptive immunoregul...

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5th International Conference on Computational Intelligence and Applications (ICCIA)

作者： Deng, Hongli Yang, Tao China West Normal Univ Educ & Informat Technol Ctr Nanchong Peoples R China

ISBN: (纸本)9781728160429

negative selection algorithm (NSA) is an important detectors training algorithm in artificial immune system (AIS). In NSAs, the self radius and location of detectors affect the performance of algorithms. However, the traditional NSAs preset the self radius empirically and generate detectors randomly without considering the distribution of antigens resulting in the performance of AIS varies greatly in different applications. To deal with these limitations, an adaptive immunoregulation based real value negative selection algorithm (AINSA) is proposed in this paper. AINSA utilizes the "adaptive immunoregulation" mechanism to calculate the self radius and optimize the location of the candidate detectors. In this way, AINSA can attain the suitable self radius for different application and effectively generate the detectors in the region where antigens distribute densely. The experimental results show, on the artificial dataset and the UCI standard datasets, AINSA can reach the higher detection rate with better detectors generation efficiency compared to the classical RNSA and V-detector algorithm.

关键词： component Artificial immune negative selection algorithm evolution immunoregulation

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Research on Intrusion Detection System based on Improved negative selection algorithm

International Core Journal of Engineering

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International Core Journal of Engineering 2021年第10期7卷 494-501页

作者： Zhihao She Mengqian Li

Through the learning and research of the immune negative selection algorithm, after learning the traditional partial matching rules, namely Hamming matching and r-continuous bit matching, summarize its advantages and improve its disadvantages to create an improved matching scheme to deal with immune negatives Select the generation of the detection set in the algorithm and the process of intrusion detection. At the same time, this paper also established an intrusion detection model based on the improved scheme and a traditional intrusion detection model, and compared the matching rate and false alarm rate (the probability of black holes) between the two models through simulation experiments. It is concluded that the matching rate and false alarm rate of the improved scheme are better than those of the traditional scheme.

关键词： Intrusion Detection Artificial Immune negative selection algorithm Custom Coding

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Clustered negative selection algorithm and fruit fly optimization for email spam detection

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第1期10卷 143-152页

作者： Chikh, Ramdane Chikhi, Salim Setif 1 Univ Fac Technol Setif Algeria Constantine 2 Univ MISC Lab Constantine Algeria

At present, spam is an actual and increasing problem that compromises email communications across the world. Thus, several solutions have been proposed to stop or reduce the amount of this threat. However, methods based on negative selection algorithm (NSA) lack continuous adaptability and suffer from low detection performance. Moreover, these methods require a large number of detectors to cover all non-self spaces. Thus, this study proposes a new e-mail detection approach based on an improved NSA called combined clustered NSA and fruit fly optimization (CNSA-FFO). The system combines actual NSA with k-means clustering and FFO to enhance the efficiency of classic NSA. Experiments results in spam benchmark show that the performance of CNSA-FFO is better than the classic NSA and NSA-PSO, especially in terms of detection accuracy, positive prediction, and computational complexity.

关键词： Artificial immune system negative selection algorithm E-mail spam Fruit fly optimization K-means clustering

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An Antigen Space Triangulation Coverage Based Real-Value negative selection algorithm

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IEEE ACCESS 2019年 7卷 51886-51898页

作者： Fan, Zhang Wen, Chen Tao, Li Cao Xiaochun Peng Haipeng Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Sichuan Univ Coll Cybersecur Chengdu 610065 Sichuan Peoples R China Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China

negative selection algorithm (NSA) is an important detector generation algorithm of artificial immune system (AIS). Traditional NSAs randomly generate detectors in the whole antigen space without considering the distribution of self/non-self antigens, therefore it is difficult for detectors to cover the whole antigen space evenly and a seriously time-consuming self-tolerance process is required. Aiming at the problem, we proposed a novel NSA based on antigen space triangulation coverage (ASTC) called ASTC-RNSA. In order to avoid the randomness in traditional NSAs, the proposed algorithm employed Delaunay Triangulation method from computational geometry to divide the self space into simplicial cells, which are utilized to determine the detector positions. Then, the overlaps between simplicial cells and self-antigens are removed to form a set of triangulation coverage areas. Finally, immune detectors which only covered non-self antigens space are generated in each triangulation coverage area. After Delaunay triangulation of antigen space, ASTC-RNSA can directly determine sui detector positions and avoid the time-consuming self-tolerance process of traditional NSAs. The analysis result shows that the time complexity of ASTC-RNSA is reduced from traditional exponential level to logarithmic level. The experimental results on several UCI datasets and artificial datasets show that the proposed algorithm can achieve more than 10 times the detector generation efficiency while maintaining similar detection performance with the most widely used representative typical algorithm, V-Detector.

关键词： negative selection algorithm detector artificial immune system Delaunay triangulation

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