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An efficient negative selection algorithm with further training for anomaly detection

KNOWLEDGE-BASED SYSTEMS 2012年 30卷 185-191页

作者： Gong, Maoguo Zhang, Jian Ma, Jingjing Jiao, Licheng Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

negative selection algorithm has been shown to be efficient for anomaly detection problems. This letter presents an improved negative selection algorithm by integrating a novel further training strategy into the training stage. The main process of further training is generating self-detectors to cover the self-region. A primary purpose of adopting further training is reducing self-samples to reduce computational cost in testing stage. It can also improve the self-region coverage. The testing stage focuses on the processing of testing samples lied within the holes. The experimental comparison among the proposed algorithm, the self-detector classification, and the V-detector on seven artificial and real-world data sets shows that the proposed algorithm can get the highest detection rate and the lowest false alarm rate in most cases. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Anomaly detection Artificial immune system Detector coverage negative selection algorithm Further training

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An immune optimization based real-valued negative selection algorithm

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APPLIED INTELLIGENCE 2015年第2期42卷 289-302页

作者： Xiao, Xin Li, Tao Zhang, Ruirui Sichuan Univ Sch Comp Sci Chengdu 610000 Peoples R China Sichuan Agr Univ Sch Business Chengdu 610000 Peoples R China

negative selection algorithms are important for artificial immune systems to produce detectors. But there are problems such as high time complexity, large number of detectors, a lot of redundant coverage between detectors in traditional negative selection algorithms, resulting in low efficiency for detectors' generation and limitations in the application of immune algorithms. Based on the distribution of self set in morphological space, the algorithm proposed in this paper introduces the immune optimization mechanism, and produces candidate detectors hierarchically from far to near, with selves as the center. First, the self set is regarded as the evolution population. After immune optimization operations, detectors of the first level are generated which locate far away from the self space and cover larger non-self space, achieving that fewer detectors cover as much non-self space as possible. Then, repeat the process to obtain the second level detectors which locate close to detectors of the first level and near the self space and cover smaller non-self space, reducing detection loopholes. By analogy, qualified detector set will be obtained finally. In detectors' generation process, the random production range of detectors is limited, and the self-reaction rate between candidate detectors is smaller, which effectively reduces the number of mature detectors and redundant coverage. Theoretical analysis demonstrates that the time complexity is linear with the size of self set, which greatly reduces the influence of growth of self scales over the time complexity. Experimental results show that IO-RNSA has better time efficiency and generation quality than classical negative selection algorithms, and improves detection rate and decreases false alarm rate.

关键词： Anomaly detection Artificial immune system negative selection algorithm Detector generation Immune optimization

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Outlier detection of GPS monitoring data using relational analysis and negative selection algorithm

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SMART STRUCTURES AND SYSTEMS 2017年第2期20卷 219-229页

作者： Yi, Ting-Hua Ye, X. W. Li, Hong-Nan Guo, Qing Dalian Univ Technol Sch Civil Engn Dalian 116023 Peoples R China Zhejiang Univ Dept Civil Engn Hangzhou 310058 Zhejiang Peoples R China

Outlier detection is an imperative task to identify the occurrence of abnormal events before the structures are suffered from sudden failure during their service lives. This paper proposes a two-phase method for the outlier detection of Global Positioning System (GPS) monitoring data. Prompt judgment of the occurrence of abnormal data is firstly carried out by use of the relational analysis as the relationship among the data obtained from the adjacent locations following a certain rule. Then, a negative selection algorithm (NSA) is adopted for further accurate localization of the abnormal data. To reduce the computation cost in the NSA, an improved scheme by integrating the adjustable radius into the training stage is designed and implemented. Numerical simulations and experimental verifications demonstrate that the proposed method is encouraging compared with the original method in the aspects of efficiency and reliability. This method is only based on the monitoring data without the requirement of the engineer expertise on the structural operational characteristics, which can be easily embedded in a software system for the continuous and reliable monitoring of civil infrastructure.

关键词： structural health monitoring global positioning system outlier detection grey relational analysis negative selection algorithm

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A combined negative selection algorithm-particle swarm optimization for an email spam detection system

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2015年 39卷 33-44页

作者： Idris, Ismaila Selamat, Ali Ngoc Thanh Nguyen Omatu, Sigeru Krejcar, Ondrej Kuca, Kamil Penhaker, Marek Univ Teknol Malaysia Fac Comp Utm Johor Bahru 81310 Johor Malaysia Univ Teknol Malaysia IRDA Digital Media Ctr Excellence Utm Johor Bahru 81310 Johor Malaysia Wroclaw Univ Technol Knowledge Management Syst Div PL-50370 Wroclaw Poland Osaka Inst Technol Fac Engn Dept Elect Informat & Commun Engn Asahi Ku Osaka 5358585 Japan Univ Hradec Kralove FIM Ctr Basic & Appl Res Hradec Kralove 50003 Czech Republic Tech Univ Ostrava VSB Fac Elect Engn & Comp Sci Dept Cybernet & Biomed Engn Ostrava 70833 Czech Republic

Email is a convenient means of communication throughout the entire world today. The increased popularity of email spam in both text and images requires a real-time protection mechanism for the media flow. The previous approach has been limited by the adaptive nature of unsolicited email spam. This research introduces an email detection system that is designed based on an improvement in the negative selection algorithm. Furthermore, particle swarm optimization (PSO) was implemented to improve the random detector generation in the negative selection algorithm (NSA). The algorithm generates detectors in the random detector generation phase of the negative selection algorithm. The combined NSA-PSO uses a local outlier factor (LOF) as the fitness function for the detector generation. The detector generation process is terminated when the expected spam coverage is reached. A distance measure and a threshold value are employed to enhance the distinctiveness between the non-spam and spam detectors after the detector generation. The implementation and evaluation of the models are analyzed. The results show that the accuracy of the proposed NSA-PSO model is better than the accuracy of the standard NSA model. The proposed model with the best accuracy is further used to differentiate between spam and non-spam in a network that is developed based on a client-server network for spam detection. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： negative selection algorithm Differential evolution Particle swarm optimization spam detectors

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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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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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Self-adaptive mobile web service discovery approach based on modified negative selection algorithm

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NEURAL COMPUTING & APPLICATIONS 2022年第3期34卷 2007-2029页

作者： Garba, Salisu Mohamad, Radziah Saadon, Nor Azizah Univ Teknol Malaysia Fac Engn Sch Comp Skudai 81310 Johor Malaysia

This paper proposes a self-adaptive mobile web service (MWS) discovery approach based on the modified negative selection algorithm (M-NSA) to improve the effectiveness and accuracy of MWS discovery in dynamic mobile environment. The main contributions of this work are the service relevance learning model and a MWS matchmaking algorithm that it is capable of changing as soon as the discovery demonstrates the feasibility of attaining improved effectiveness or accuracy. This is achieved by transforming the two stages of modified negative selection algorithm (M-NSA) into service relevance and self-adaptive matchmaking, respectively. The proposed approach is evaluated in terms of both binary and graded relevance. After an experiment with the largest MWS dataset, the proposed approach records better results in comparison with the state-of-the-art approaches. This is owing to the self/nonself discrimination mechanism, in addition to the decent parameter analysis, and the use of more comprehensive information that covers the entire discovery space.

关键词： Self-adaptive Mobile web service Service discovery negative selection algorithm Dynamic mobile environment

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An antigen space density based real-value negative selection algorithm

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APPLIED SOFT COMPUTING 2017年 61卷 860-874页

作者： Yang, Tao Chen, Wen Li, Tao Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China

The negative selection algorithm (NSA) is an important detector generation algorithm for artificial immune systems. In high-dimensional space, antigens (data samples) distribute sparsely and unevenly, and most of them reside in low-dimensional subspaces. Therefore, traditional NSAs, which randomly generate detectors without considering the distribution of the antigens, cannot effectively distinguish them. To overcome this limitation, the antigen space density based real-value NSA (ASD-RNSA) is proposed in this paper. The ASD-RNSA contains two new processes. First, in order to improve detection efficiency, ASD-RNSA utilizes the antigen space density to calculate the low-dimensional subspaces where antigens are densely gathered and directly generate detectors in these subspaces. Second, to eliminate redundant detectors and prevent the algorithm from prematurely converging in high-dimensional space, ASD-RNSA suppresses candidate detectors that are recognized by other mature detectors and adopts an "antibody suppression rate" to replace the "expected coverage" as the termination condition. Experimental results show that ASD-RNSA achieves a better detection rate and has better generation quality than classical real-value NSAs. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Artificial immune system negative selection algorithm Detector Subspace Antibody suppression

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An immunological approach based on the negative selection algorithm for real noise classification in speech signals

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2017年 72卷 125-133页

作者： Enside de Abreu, Caio Cesar Queiroz Duarte, Marco Aparecido Villarreal, Francisco Univ Estado Mato Grosso Dept Comp UNEMAT Santa Rita St 128 Alto Araguaia Brazil Sao Paulo State Univ UNESP Dept Elect Engn Brasil Ave 56 Ilha Solteira Brazil Univ Estadual Mato Grosso do Sul UEMS Dept Math Rodovia MS 306 KM 06 Cassilandia Brazil Sao Paulo State Univ UNESP Dept Math Brasil Ave 56 Ilha Solteira Brazil

This paper presents a new approach to detect and classify background noise in speech sentences based on the negative selection algorithm and dual-tree complex wavelet transform. The energy of the complex wavelet coefficients across five wavelet scales are used as input features. Afterward, the proposed algorithm identifies whether the speech sentence is, or is not, corrupted by noise. In the affirmative case, the system returns the type of the background noise amongst the real noise types considered. Comparisons with classical supervised learning methods are carried out. Simulation results show that the artificial immune system proposed overcomes classical classifiers in accuracy and capacity of generalization. Future applications of this tool will help in the development of new speech enhancement or automatic speech recognition systems based on noise classification. (C) 2016 Elsevier GmbH. All rights reserved.

关键词： Noise classification Speech enhancement Artificial immune systems negative selection algorithm Dual-tree complex wavelet transform

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A novel negative selection algorithm with hyper-rectangle detectors based on full coverage of state space for anomaly detection

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期45卷 719-730页

作者： Gu, Ming Li, Dong Gong, Lanlan Liu, Jia Liu, Shulin Changzhou Univ Sch Petr Engn Changzhou Peoples R China Shanghai Univ Sch Mech Engn & Automat Shanghai Peoples R China

The traditional negative selection algorithm with a randomly generated hypersphere detector is unable to satisfy the development needs of continuous learning due to the inherent defects of the detector. This paper proposes a novel negative selection algorithm for hyper-rectangle detectors that overcomes the shortcomings of randomly generated hyper-sphere detectors and lays the foundation for a negative selection algorithm with continuous learning capability. It uses self-sample clusters of equal-sized hypercubes instead of self-samples for training. The hyper-rectangle detectors are generated by cutting the nonself-space along the boundary of the self-sample clusters. The state space is covered without overlapping each other by self-sample clusters and detectors. The anomaly detection performance of the proposed method was demonstrated using Iris data, vowel recognition data (Vowel), and Wisconsin Breast Cancer (BCW) data. The experimental results show that the proposed method outperforms other artificial immune algorithms and clustering algorithms under the same parameter conditions.

关键词： Artificial immune algorithm negative selection algorithm anomaly detection hyper-rectangle detectors artificial intelligence

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