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China Ocean Engineering 2012年第3期26卷 379-395页

作者： Mojtahedi A. Lotfollahi Yaghin M.A. Hassanzadeh Y. Abbasidoust F. Ettefagh M.M. Aminfar M.H. Faculty of Civil Engineering University of TabrizTabriz 51666-14766Iran Faculty of Mechanical Engineering University of TabrizTabriz 51666-14766Iran

Steel jacket-type platforms are the common kind of the offshore structures and health monitoring is an important issue in their safety assessment. In the present study, a new damage detection method is adopted for this kind of structures and inspected experimentally by use of a laboratory model. The method is investigated for developing the robust damage detection technique which is less sensitive to both measurement and analytical model uncertainties. For this purpose, incorporation of the artificial immune system with weighted attributes （AISWA） method into finite element （FE） model updating is proposed and compared with other methods for exploring its effectiveness in damage identification. Based on mimicking immune recognition, noise simulation and attributes weighting, the method offers important advantages and has high success rates. Therefore, it is proposed as a suitable method for the detection of the failures in the large civil engineering structures with complicated structural geometry, such as the considered case study.

关键词： structural health monitoring jacket-type platforms artificial immune system FEM modal test

A novel algorithm of artificial immune system for high-dimensional function numerical optimization

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Progress in Natural Science:Materials International 2005年第5期15卷 463-471页

作者： DU Haifeng 1,2*, GONG Maoguo1, JIAO Licheng1 and LIU Ruochen1(1. Institute of Intelligent Information Processing and Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China 2. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China) Institute of Intelligent Information Processing and Key Laboratory of Radar Signal Processing Xidian University Xi''an 710071 China School of Mechanical Engineering Xi''an Jiaotong University Xi''an 710049 China

Based on the clonal selection theory and immune memory theory, a novel artificial immune system algorithm, immune memory clonal programming algorithm (IMCPA), is put forward. Using the theorem of Markov chain, it is proved that IMCPA is convergent. Compared with some other evolutionary programming algorithms (like Breeder genetic algorithm), IMCPA is shown to be an evolutionary strategy capable of solving complex machine learning tasks, like high-dimensional function optimization, which maintains the diversity of the population and avoids prematurity to some extent, and has a higher convergence speed.

关键词： clonal selection immune memory artificial immune system evolutionary algorithms Markov chain.

Smartphone Malware Detection Model Based on artificial immune system

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China Communications 2014年第A01期11卷 86-92页

作者： WU Bin LU Tianliang ZHENG Kangfeng ZHANG Dongmei LIN Xing InformationSecurityLaboratory NationalDisasterRecoveryTechnologyEngineeringLaboratoryBeijingUniversityofPostsandTelecommunicationsBeijing100876P.R.China People'sPublicSecurityUniversityofChina Beijing100038P.R.China

In order to solve the problem that me traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model （SP-MDM） based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation.

关键词： artificial immune system smartphonemalware detection negative selection clonalselection

Combining artificial immune system and Clustering Analysis: A Stock Market Anomaly Detection Model

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Journal of Intelligent Learning systems and Applications 2020年第4期12卷 83-108页

作者： Liam Close Rasha Kashef Electrical Computer and Biomedical Engineering Department Ryerson University Toronto Canada

artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and re-al-time data transactions, the stock market has increased vulnerability to at-tacks. This paper aims to detect these attacks based on normal trade behavior using an artificial immune system (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipula-tions are not available for training. This paper discovers a competitive alterna-tive to the leading approach and investigates the effects of combining AIS with clustering algorithms;Kernel Density Estimation, Self-Organized Maps, Densi-ty-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.

关键词： artificial immune system Clustering Anomaly Detection Financial Data

A Review of artificial immune system Based Security Frameworks for MANET

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International Journal of Communications, Network and system Sciences 2016年第1期9卷 1-18页

作者： Lincy Elizebeth Jim Mark A. Gregory RMIT University Melbourne Australia

Mobile ad hoc networks (MANETs) are collections of wireless mobile devices that form a communication network with restricted broadcast range, limited resources and without fixed infrastructure. Routing is a critical function in multi-hop MANETs. At the same time, security in MANETs—especially routing security—presents a number of new and interesting challenges. Communication is achieved by relaying data along routes that are dynamically discovered and maintained through collaboration between the nodes. Advances in the field of artificial immune systems provide an opportunity to improve MANET security and performance. artificial immune systems mimic the functionality of the human immune system wherein there is clear distinction between self and non self and this delineation is important in a MANET where there is no centralized management. The high level of protection provided to the human body by an evolved immune system can be applied as a security feature in MANET. The current security techniques proposed for MANET have varying degrees of success due to the dynamic nature of a MANET. This paper will review different strategies for the application of artificial immune systems to MANETs.

关键词： artificial immune system MANET Security Routing

RBF Neural Network Based RFID Indoor Localization Method Using artificial immune system

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RBF Neural Network Based RFID Indoor Localization Method Usi...

Associative Classification Based on artificial immune system

第30届中国控制与决策会议

作者： Zhonghua Li Guannan He Muyuan Li Le Ma Qiaofang Chen Jinqiu Huang Jianzhong Cao Shuo Feng Haijian Gao Shanjin Wang School of Electronics and Information Technology Sun Yat-sen University Guangzhou College of South China University of Technology School of Electronic Information and Electrical Engineering Huizhou University School of Electrical Engineering and Intelligentization Dongguan University of Technology

Indoor location based service is more and more popular in our daily lives. Due to such advantages of low power consumption, large interrogation range and low deployment cost, the Radio Frequency IDentification（RFID） becomes a simple, cost-effective indoor localization technology in indoor location based service systems. However, some existing RFID indoor localization methods are yet not satisfactory in accuracy. This paper proposes an artificial immune system based Radial Basis Function Neural Network（AIS-RBF-NN） to realize RFID indoor localization, in which artificial immune system is used to optimize the center vector selection of radial basis functions. To counteract the effect of the scene noise, the differences between the Received Signal Strength Indication（RSSI） values are fed into AIS-RBFNN as well as RSSI values themselves. Simulation results indicate that the proposed AIS-RBF-NN based RFID indoor localization method is effective and outperforms the existing method.

关键词： artificial immune system center vector selection indoor localization RBF neural network RFID RSSI

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Associative Classification Based on Artificial Immune System

Research on immune Pathology in artificial immune system

2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Zhang Lei Jiang Ji Min School of Electronic Information Engineering Henan University of Science and Technology

Associative classification algorithms which are based on association rules have performed well compared with other classification approaches. However a fundamental limitation with these classification algorithms is that the search space of candidate rules is very large and the processes of rule discovery and rule selection are conducted separately. This paper proposes an approach called ARMBIS, which is based on the natural immune principle, for searching associative rules. The proposed algorithm has the capability of dealing with complex search space of association rules while still ensuring that the resultant set of association rules is appropriate for associative classification. The performance evaluation results have shown that the proposed algorithm has achieved good runtime and accuracy performance in comparison with conventional associative classification algorithms.

关键词： Associative classification association rules artificial immune system

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Research on Immune Pathology in Artificial Immune System

Feature selection for chemical process fault diagnosis by artificial immune systems

2009中国控制与决策会议

作者： Xuanwu Zhou Key Laboratory of Network & Information Security of the APF,Engineering College of the APF,Xi''an 710086,P.R.China

＜正＞artificial immune system（AIS） mimicks the superior properties of biological immune system and provides an effective method in intelligent computing and intelligent system *** the disease-causing mechanisms of immune pathology have led to sever security problems in artificial immune *** the paper,we analyzed the basic principles of immune protection and immune pathology of biological system considering its application in artificial immune *** we take artificial immune defending system as an example to analyze the cause and potential influence of immune pathology on *** to the different security problems from immunodeficiency,hypersensitivity and autoimmunity,we put forward corresponding measures to reinforce the security,robustness and stability of artificial immune system and thus effectively avoid these problems.

关键词： immune pathology artificial immune system negative selection algorithm immunodeficiency system efficiency

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Chinese Journal of Chemical Engineering 2018年第8期26卷 1599-1604页

作者： Liang Ming Jinsong Zhao Department of Chemical Engineering Tsinghua University Beijing Key Laboratory of Industrial Big Data system and Application Tsinghua University

With the Industry 4.0 era coming, modern chemical plants will be gradually transformed into smart factories, which sets higher requirements for fault detection and diagnosis(FDD) to enhance operation safety intelligence. In a typical chemical process, there are hundreds of process variables. Feature selection is a key to the efficiency and effectiveness of FDD. Even though artificial immune system has advantages in adaptation and independency on a large number of fault samples, antibody library construction used to be based on experience. It is not only time consuming, but also lack of scientific foundation in fault feature selection, which may deteriorate the FDD performance of the AIS. In this paper, a fault antibody feature selection optimization(FAFSO) algorithm is proposed based on genetic algorithm to optimize the fault antibody features and the antibody libraries' thresholds simultaneously. The performance of the proposed FAFSO algorithms is illustrated through the Tennessee Eastman benchmark problem.

关键词： artificial immune system Genetic algorithm Feature selection

Use of self-organizing suppression and q-Gaussian mutation in artificial immune systems

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International Journal of Intelligent Computing and Cybernetics 2013年第3期6卷 296-322页

作者： Helder Ken Shimo Renato Tinòs Department of Computing and Mathematics University of Sa˜o PauloSa˜o PauloBrazil

Purpose-The purpose of this paper is to propose two operators for diversity and mutation control in artificial immune systems(AISs).Design/methodology/approach-The proposed operators are applied in substitution to the suppression and mutation operators used in *** proposed mechanisms were tested in the opt-aiNet,a continuous optimization algorithm inspired in the theories of *** traditional opt-aiNet uses a suppression operator based on the immune network principles to remove similar cells and add random ones to control the diversity of the *** procedure is computationally expensive,as the Euclidean distances between every possible pair of candidate solutions must be *** work proposes a self-organizing suppression mechanism inspired by the self-organizing criticality(SOC)phenomenon,which is less dependent on parameter *** work also proposes the use of the q-Gaussian mutation,which allows controlling the form of the mutation distribution during the optimization *** algorithms were tested in a well-known benchmark for continuous optimization and in a bioinformatics problem:the rigid docking of ***-The proposed suppression operator presented some limitations in unimodal functions,but some interesting results were found in some highly multimodal *** proposed q-Gaussian mutation presented good performance in most of the test cases of the benchmark,and also in the docking ***/value-First,the self-organizing suppression operator was able to reduce the complexity of the suppression stage in the ***,the use of q-Gaussian mutation in AISs presented better compromise between exploitation and exploration of the search space and,as a consequence,a better performance when compared to the traditional Gaussian mutation.

关键词： artificial immune system Evolutionary computation Self-adjusting systems