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A negative selection algorithm with online adaptive learning under small samples for anomaly detection

NEUROCOMPUTING 2015年第PartB期149卷 515-525页

作者： Li, Dong Liu, Shulin Zhang, Hongli Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Changzhou Univ Sch Petr Engn Changzhou 213164 Peoples R China

The training stage and testing stage of traditional negative selection algorithm (NSA) are mutually independent, and NSA lacks continuous learning ability. Its detector cannot completely cover the non-self space. A new NSA with online adaptive learning under small training samples, OALI-detector, was proposed in this paper. I-detector can fully separate the self space from the non-self space with an appropriate self radius. It can adapt itself to real-time change of self space during the testing stage. The experimental comparison among I-detector, V-detector, and other anomaly detection algorithms in two artificial and Iris datasets shows that the I-detector can obtain the highest detection rate in most cases. The experimental comparison between OALI-detector and V-detector on Iris datasets shows that when overfitting does not occur, the OALI-detector can obtain the highest and lowest false alarm rates, even if only one self sample is used for training. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Artificial immune system negative selection algorithm Anomaly detection Interface detector Online adaptive learning

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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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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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An Improved Detector Set Generation Mechanism in negative selection algorithm

An Improved Detector Set Generation Mechanism in Negative Se...

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2016 3rd International Conference on Information and Communication Technology for Education(ICTE 2016)

作者： Bo Hu Science and Technology College Jiangxi Normal University

in view of the shortcomings of the detector generation mechanism in the existing negative selection algorithm,a new method of boundary detection is proposed to deal with the problem of the boundary *** the algorithm,it is used as the self region of the body and its adjacent *** realization process and advantages of the algorithm are *** algorithm is validated by the synthetic data set 2 DSynthetic Data and the actual Iris data set and the Biomedical data *** results show that the detection rate of this algorithm is high,especially the points that are in the self and non self boundary can be detected effectively.

关键词： negative selection algorithm Boundary detection Detector Detection rate

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Integrated negative selection algorithm and Positive selection algorithm for Malware Detection

Integrated Negative Selection Algorithm and Positive Selecti...

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Fuyong Zhang Ying Ma School of Computer Science and Network Security Dongguan University of Technology School of Computer and Information Engineering Xiamen University of Technology

Run-time malware detection strategies are efficient and robust, which get more and more attention. In this paper, we use I/O Request Package（IRP） sequences for malware detection. N-gram will be used to analyze IRP sequences for feature extraction. Integrated negative selection algorithm（NSA） and Positive selection algorithm（PSA）, through a selection of ngram sequences which only exist in malware IRP sequences, we have more than 96% true positive rate and 0% false positive rate.

关键词： I/O Request Package artificial immune system negative selection algorithm positive selection algorithm malware detection

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Motor Fault Detection and Diagnosis Based on negative selection algorithm

Motor Fault Detection and Diagnosis Based on Negative Select...

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2015 5th International Conference on Information Engineering for Mechanics and Materials(ICIMM 2015)

作者： Lihua Zhou Zhongjian Dai Yaping Dai Linhui Zhao School of Automation Beijing Institute of Technology School of Mechatronics Beijing Union University

This paper presents a motor fault diagnosis method based on negative selection algorithm. It has the structure of two-level detectors, the first level detector detecting the presence of faults and the second level detector detecting the type of faults. Therefore the first level detectors are trained by using motor normal signals, and the second level detectors are trained by using several types of fault signals. During the process of detecting, only the test results of first level detectors are abnormal, the second level detectors are activated and implement fault detection to identify fault type. In this paper, normal vibration signals of motor bearing and three types of fault signals from American Case Western Reserve University bearing fault database are used to verify the fault diagnosis method. The experimental results show that the method can effectively detect early failure and can correctly identify the fault type.

关键词： artificial immune system negative selection algorithm fault detection fault diagnosis

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Hybrid email spam detection model with negative selection algorithm and differential evolution

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年第0期28卷 97-110页

作者： Idris, Ismaila Selamat, Ali Omatu, Sigeru Univ Teknol Malaysia Knowledge Econ Res Alliance SERG Johor Baharu 81310 Johor Malaysia Univ Teknol Malaysia Fac Comp Johor Baharu 81310 Johor Malaysia Dept Elect Informat & Commun Engn Asahi Ku Osaka 5358585 Japan

Email spam is an increasing problem that not only affects normal users of internet but also causes a major problem for companies and organizations. Earlier techniques have been impaired by the adaptive nature of unsolicited email spam. Inspired by adaptive algorithm, this paper introduces a modified machine learning technique of the human immune system called negative selection algorithm (NSA). A local selection differential evolution (DE) generates detectors at the random detector generation phase of NSA;code named NSA-DE. Local outlier factor (LOF) is implemented as fitness function to maximize the distance of generated spam detectors from the non-spam space. The problem of overlapping detectors is also solved by calculating the minimum and maximum distance of two overlapped detectors in the spam space. From the experiments, the results show that the detection accuracy of NSA-DE is 83.06% while the standard negative selection algorithm is 68.86% at 7000 generated detectors. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： negative selection algorithm Differential evolution email Spam Non-spam Detector generation

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Improved email spam detection model with negative selection algorithm and particle swarm optimization

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APPLIED SOFT COMPUTING 2014年 22卷 11-27页

作者： Idris, Ismaila Selamat, Ali Univ Teknol Malaysia UTM IRDA Digital Media COE Off Res Alliance Utm Johor Bahru 81310 Johor Malaysia Univ Teknol Malaysia Fac Comp Utm Johor Bahru 81310 Johor Malaysia

The adaptive nature of unsolicited email by the use of huge mailing tools prompts the need for spam detection. Implementation of different spam detection methods based on machine learning techniques was proposed to solve the problem of numerous email spam ravaging the system. Previous algorithm used in email spam detection compares each email message with spam and non-spam data before generating detectors while our proposed system inspired by the artificial immune system model with the adaptive nature of negative selection algorithm uses special features to generate detectors to cover the spam space. To cope with the trend of email spam, a novel model that improves the random generation of a detector in negative selection algorithm (NSA) with the use of stochastic distribution to model the data point using particle swarm optimization (PSO) was implemented. Local outlier factor is introduced as the fitness function to determine the local best (Pbest) of the candidate detector that gives the optimum solution. Distance measure is employed to enhance the distinctiveness between the non-spam and spam candidate detector. The detector generation process was terminated when the expected spam coverage is reached. The theoretical analysis and the experimental result show that the detection rate of NSA-PSO is higher than the standard negative selection algorithm. Accuracy for 2000 generated detectors with threshold value of 0.4 was compared. negative selection algorithm is 68.86% and the proposed hybrid negative selection algorithm with particle swarm optimization is 91.22%. (C) 2014 Elsevier B.V. All rights reserved.

关键词： negative selection algorithm Particle swarm optimization Email Spam Non-spam Detector generation

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Improved thresholding based on negative selection algorithm (NSA)

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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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DGA-PSO: An improved detector generation algorithm based on particle swarm optimization in negative selection

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KNOWLEDGE-BASED SYSTEMS 2023年第1期278卷

作者： Zhang, Ge He, Junjiang Li, Wenshan Li, Tao Lan, Xiaolong Wang, Yunpeng Sichuan Univ Sch Cyber Sci & Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China Chengdu Univ Informat Technol Sch Cyber Sci & Engn 24 Sect 1Xuefu Rd Southwest Airport Econ Dev Zon Chengdu 610225 Peoples R China

The negative selection algorithm (NSA) is an essential algorithm in the artificial immune system used to achieve anomaly detection by generating detectors. The traditional NSA algorithm generates candidate detectors randomly, which leads to a partially dense and redundant distribution of detectors in the nonself areas, resulting in the presence of holes that are not covered by detectors. A detector generation algorithm based on particle swarm optimization (DGA-PSO) is proposed to overcome these defects. DGA-PSO converts the self-tolerance process into an adaptation function to guide particles to move in a specific direction by artificial settings and variants, generates efficient detectors covering the nonself space, reduces the redundancy among detectors and fills holes not covered. Thus, we successfully reduce the number of detectors while improving the detection rate of the algorithm. Through experimental validation analysis, DGA-PSO ranks first in detector training time and the detection rate on four UCI datasets compared to the classical algorithms RNSA and V-Detector and the improved algorithms BIORV-NSA, ADC-NSA and IFB-NSA.& COPY;2023 Elsevier B.V. All rights reserved.

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

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