Showing papers on "Artificial immune system published in 2002"

Learning and optimization using the clonal selection principle

Abstract: The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens (Ag's) are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective Ag's. This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The general algorithm, named CLONALG, is derived primarily to perform machine learning and pattern recognition tasks, and then it is adapted to solve optimization problems, emphasizing multimodal and combinatorial optimization. Two versions of the algorithm are derived, their computational cost per iteration is presented, and a sensitivity analysis in relation to the user-defined parameters is given. CLONALG is also contrasted with evolutionary algorithms. Several benchmark problems are considered to evaluate the performance of CLONALG and it is also compared to a niching method for multimodal function optimization.

Artificial Immune Systems: A New Computational Intelligence Approach

Abstract: Introduction.- Fundamentals of the Immune System.- A Framework for Engineering Artificial Immune Systems.- A Survey of Artificial Immune Systems.- The Immune System in Context with Other Biological Systems.- AIS in Context with Other Computational Intelligence Paradigms.- Case Studies.- Conclusions and Future Trends.- References.- Appendix I: Glossary of Biological Terms.- Appendix II: Pseudocode for Immune Algorithms.- Appendix III: WEB Resources on Artificial Immune Systems. Index.

An artificial immune network for multimodal function optimization

Abstract: This paper presents the adaptation of an immune network model, originally proposed to perform information compression and data clustering, to solve multimodal function optimization problems. The algorithm is described theoretically and empirically compared with similar approaches from the literature. The main features of the algorithm include: automatic determination of the population size, combination of local with global search (exploitation plus exploration of the fitness landscape), defined convergence criterion, and capability of locating and maintaining stable local optima solutions.

Artificial Immune Systems: A New Computational Intelligence Paradigm

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An artificial immune system architecture for computer security applications

Abstract: With increased global interconnectivity and reliance on e-commerce, network services and Internet communication, computer security has become a necessity Organizations must protect their systems from intrusion and computer virus attacks Such protection must detect anomalous patterns by exploiting known signatures while monitoring normal computer programs and network usage for abnormalities Current anti-virus and network intrusion detection (ID) solutions can become overwhelmed by the burden of capturing and classifying new viral strains and intrusion patterns To overcome this problem, a self-adaptive distributed agent-based defense immune system based on biological strategies is developed within a hierarchical layered architecture A prototype interactive system is designed, implemented in Java and tested The results validate the use of a distributed-agent biological system approach toward the computer security problems of virus elimination and ID

aiNet: An Artificial Immune Network for Data Analysis

Abstract: This chapter shows that some of the basic aspects of the natural immune system discussed in the previous chapter can be used to propose a novel artificial immune network model with the main goals of clustering and filtering crude data sets described by high-dimensional samples. Our aim is not to reproduce with confidence any immune phenomenon, but demonstrate that immune concepts can be used as inspiration to develop novel computational tools for data analysis. As important results of our model, the network evolved will be capable of reducing redundancy and describing data structure, including their spatial distribution and cluster interrelations. Clustering is useful in several exploratory pattern analyses, grouping, decision-making and machine-learning tasks, including data mining, knowledge discovery, document retrieval, image segmentation and automatic pattern classification. The data clustering approach was implemented in association with hierarchical clustering and graphtheoretical techniques, and the network performance is illustrated using several benchmark problems. The computational complexity of the algorithm and a detailed sensitivity analysis of the user-defined parameters are presented. A trade-off among the proposed model for data analysis, connectionist models (artificial neural networks) and evolutionary algorithms is also discussed.

A resource limited artificial immune classifier

Abstract: This paper presents a new supervised learning paradigm inspired by mechanisms exhibited in immune systems. This work provides an explication of a resource limited artificial immune classification algorithm, named AIRS (Artificial Immune Recognition System), and provides results on simulated data sets to demonstrate the fundamental behavior of the algorithm.

The Danger Theory and Its Application to Artificial Immune Systems

Abstract: Over the last decade, a new idea challenging the classical self-non-self viewpoint has become popular amongst immunologists. It is called the Danger Theory. In this conceptual paper, we look at this theory from the perspective of Artificial Immune System practitioners. An overview of the Danger Theory is presented with particular emphasis on analogies in the Artificial Immune Systems world. A number of potential application areas are then used to provide a framing for a critical assessment of the concept, and its relevance for Artificial Immune Systems.

Towards an artificial immune system for network intrusion detection: an investigation of dynamic clonal selection

Abstract: One significant feature of artificial immune systems is their ability to adapt to continuously changing environments, dynamically learning the fluid patterns of 'self' and predicting new patterns of 'non-self'. This paper introduces and investigates the behaviour of dynamiCS, a dynamic clonal selection algorithm, designed to have such properties of self-adaptation. The effects of three important system parameters: tolerisation period, activation threshold, and life span are explored. The abilities of dynamiCS to perform incremental learning on converged data, and to adapt to novel data are also demonstrated.

Combining negative selection and classification techniques for anomaly detection

Abstract: This paper presents a novel approach inspired by the immune system that allows the application of conventional classification algorithms to perform anomaly detection. This approach appears to be very useful where only positive samples are available to train an anomaly detection system. The proposed approach uses the positive samples to generate negative samples that are used as training data for a classification algorithm. In particular, the algorithm produces fuzzy characterization of the normal (or abnormal) space. This allows it to assign a degree of normalcy, represented by membership value, to elements of the space.

Coverage and generalization in an artificial immune system

Abstract: LISYS is an artificial immune system framework which is specialized for the problem of network intrusion detection. LISYS learns to detect abnormal packets by observing normal network traffic. Because LISYS sees only a partial sample of normal traffic, it must generalize from its observations in order to characterize normal behavior correctly. A variation of the r-contiguous bits matching rule is introduced, and its effect on coverage and generalization is studied. The effect of representation diversity on coverage and generalization is also explored by studying permutations in the order of bits in the representation.

Artificial Immune Systems: A New Computational Approach

Abstract: When one reads through the current literature on artificial immune systems (AIS), a common observation is: ''the field of AIS is too young to be well defined, and its scope and limitations are still unknown''. Indeed, as remarked by I. Stewart while referring to chaos in his best seller Does God Play Dice? ''few were willing to offer a precise definition. This isn't unusual in a 'hot' research area - it's hard to define something when you feel you still don't fully understand it.'' Despite this, we feel that in order to help promote and consolidate the emerging area of AIS, an attempt should be made at drawing together what sometimes seems to be very disparate work. In this book we make an effort to bring many ideas from AIS together into a single text that can provide some basics for AIS. It is our hope that this will make the field more accessible to the wider community and also begin the process of formalizing AIS through the introduction of an engineering framework. The majority of the ideas contained in this volume constitute an outcome of Leandro's Ph.D. thesis undertaken at the State University of Campinas - Unicamp, Brazil. Between the two of us, we reviewed, extended, discussed, and improved these ideas in order to achieve the final result that is now this book. The motivation to write this book came from several parts: from the referees of Leandro's viva, from several conversations we had with one another, and from comments of many researchers and research students in the broad area of computational intelligence. Most importantly, we felt that the field lacked a textbook, but we (of course!) do not claim this text is going to answer all questions. Indeed, we see this very much as a first attempt and hopefully not the last one. We hope it will help to mature the field and inspire researchers in AIS and many other areas to gain a better understanding of such a new, rich, and exciting research area. In order to set the scene for our book, we begin discussing themes such as computing with biological metaphors and computational intelligence. There then follows a discussion on the fundamentals of the biological immune system. It was very difficult to decide how deep we should go into biological terminology, and it is possible that some readers may think that we have gone a bit over the top. However, we feel what we have produced is a compromise of offering a text that makes the biological language simple for computer scientists and engineers, but one that is accurate and provides enough terminology so as to prepare the reader to understand the contents of the book itself and also the related literature on AIS. From biology, there emerges a proposed framework on how to engineer an AIS. We observed from the literature that there were a number of common building blocks, which would make an ideal common framework to design AIS. We then try to exhaustively survey the publications on AIS. Instead of briefly describing every work cited, we identify the major application domains, describe one work of each research school and reference the others. We focus on the immune metaphors employed by the authors and how their approaches suit the framework introduced. We then again turn back to biology. Chapter 5 is strongly biological and one would probably raise the question ''do we actually need all this biology?'' It is worth noting that by presenting biology in a broader context, it allows us to understand the wider picture played by the immune system with other organisms. When viewed in relation to other systems, the evolution of species, and cognition, it is easier to explain some of the immune system's behavior and to compare this behavior with the behavior of other systems. Chapter 5 also serves the purpose of reviewing the biological motivation for the development of several computational intelligence tools, such as neural networks and evolutionary algorithms, to be discussed in Chapter 6. Artificial immune systems are hybrid systems almost by their very nature, and thus, this book could not restrict itself to a discussion of this single theme. It goes far beyond the AIS domain and discusses several other computational intelligence paradigms. Among these, we focus on artificial neural networks and evolutionary algorithms. One of our motivating factors for this is the fact that it is not unusual to hear questions concerning the distinction between an AIS and a genetic algorithm, immune network models and neural networks, and so on. One point to note is that, the flavor of the book might be seen deliberately philosophical in parts. This is an attempt on our end to place emphasis on underlying concepts, knowing that in this rapidly developing area the specifics may change very quickly. That's what our book is about; computational intelligence focused on the emerging field of artificial immune systems. We hope that it helps to shape the field and that it serves as a guide for you to understand and engineer your own AIS. Leandro Nunes de Castro & Jonathan Timmis Canterbury, April 2002

Immunotronics - novel finite-state-machine architectures with built-in self-test using self-nonself differentiation

Abstract: A novel approach to hardware fault tolerance is demonstrated that takes inspiration from the human immune system as a method of fault detection. The human immune system is a remarkable system of interacting cells and organs that protect the body from invasion and maintains reliable operation even in the presence of invading bacteria or viruses. This paper seeks to address the field of electronic hardware fault tolerance from an immunological perspective with the aim of showing how novel methods based upon the operation of the immune system can both complement and create new approaches to the development of fault detection mechanisms for reliable hardware systems. In particular, it is shown that by use of partial matching, as prevalent in biological systems, high fault coverage can be achieved with the added advantage of reducing memory requirements. The development of a generic finite-state-machine immunization procedure is discussed that allows any system that can be represented in such a manner to be "immunized" against the occurrence of faulty operation. This is demonstrated by the creation of an immunized decade counter that can detect the presence of faults in real time.

A new classifier based on resource limited artificial immune systems

Abstract: This paper presents a new tool for supervised learning, modeled on resource limited Artificial Immune Systems. A supervised learning system, it is self-regulatory, efficient, and stable under a wide range of user-set parameters. Its performance is comparable to well-established classifiers on a variety of testbeds, including the iris data, the diabetes classification problem, the ionosphere problem, and the rock/metal classification problem for mine detection.

Revisiting LISYS: parameters and normal behavior

Abstract: This paper studies a simplified form of LISYS, an artificial immune system for network intrusion detection. The paper describes results based on a new, more controlled data set than that used for earlier studies. The paper also looks at which parameters appear most important for minimizing false positives, as well as the trade-offs and relationships among parameter settings.

Integrating artificial immune algorithms for intrusion detection

Abstract: This thesis focuses on the combination of a set of artificial immune algorithms and their application to intrusion detection. Three evolutionary algorithms are investigated, each based on a process from the human immune system. It is demonstrated that these three algorithms, negative selection, clonal selection and gene library evolution, lead to self-organisation in the artificial immune system (AIS). In addition, the attributes required for effective intrusion detection are analysed in depth. With the aim of intrusion detection in mind, novel variations of the algorithm are created and tested on different data sets, including real network traffic data. This thesis makes the following eight main contributions. 1. The components of human immune systems that are crucial to the improvement of AIS for intrusion detection are identified. 2. A systematic framework for an AIS for network intrusion detection is introduced by combining three evolutionary stages: negative selection, clonal selection and gene library maintenance. It is demonstrated that this framework can fulfil the role of a network-based intrusion detection system. 3. It is demonstrated that the negative selection algorithm employed for the thesis has a severe scaling problem when applied in a real network environment. 4. It is demonstrated that a static clonal selection algorithm with a negative selection operator achieves efficient niche maintenance and acceptable self-tolerance. 5. A dynamic clonal selection algorithm that combines three evolutionary stages allows the AIS to be adaptable to dynamically changing antigen behaviours. 6. The effect of three parameters on the behaviour of the dynamic clonal selection algorithm is analysed. These parameters are: tolerisation period, activation threshold and life span. Satisfactory TP and FP rates are obtained by setting these parameters to appropriate values. 7. The extension of the dynamic clonal selection algorithm to employ deletion of memory detectors reduces high FP rates observed when previously observed normal behaviours no longer represent normal behaviours. 8. It is demonstrated that simulation of gene library evolution using hypermutation reduces the amount of costimulation (human intervention). These contributions support the conclusion of this thesis: that an artificial immune model harnessing the three evolutionary stages demonstrates adaptability to continuously changing environments, dynamically learning the fluid patterns of 'self, and detecting new patterns of 'non-self'.

The fuzzy artificial immune system: motivations, basic concepts, and application to clustering and Web profiling

Abstract: The human immune system can be seen as a complex network structure that is able to respond to an almost unlimited multitude of foreign invaders such as viruses and bacteria. Hence, this parallel and distributed adaptive system promises tremendous potential in many intelligent computing applications, including Web mining. Some of these immunity-based techniques involve the development and analysis of algorithms that can identify patterns in observed data in order to make predictions about unseen data. In this paper, we introduce several new enhancements to deal with some of the weaknesses of previous artificial immune system models. In particular, we address the uncertainty and fuzziness inherent in the matching process that takes place between antibodies and antigens. This problem is handled by introducing a fuzzy artificial immune system. A fuzzy artificial immune system mimicking the body's adaptive learning and defense mechanism in the face of invading biological agents is used as a monitoring and learning system for a Web site in the face of all incoming Web requests.

Clonal operator and antibody clone algorithms

Abstract: Based on clonal selection theory, the main mechanisms of a clone in the immune system, which are explored in the field of artificial intelligence, are analyzed. An artificial immune system algorithm, antibody clone algorithm, is put forward. Compared with an improved gene algorithm, the new algorithm is shown to be an evolutionary strategy capable of solving complex machine learning tasks, like multi-modal optimization. Using Markov chain theories, it is proved that the antibody clone algorithm is convergent.

An artificial immune system for continuous analysis of time-varying data

Abstract: M Neal, An Artificial Immune System for Continuous Analysis of Time-Varying Data, in Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS), 2002, eds J Timmis and P J Bentley, volume 1, pages 76-85,

A recommender system based on the immune network

Abstract: The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an artificial immune system (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by collaborative filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: antigen-antibody interaction for matching and antibody-antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.

Artificial Immune Systems: A Novel Approach to Pattern Recognition

Abstract: This chapter introduces a new computational intelligence paradigm to perform pattern recognition, named Artificial Immune Systems (AIS). AIS take inspiration from the immune system in order to build novel computational tools to solve problems in a vast range of domain areas. The basic immune theories used to explain how the immune system perform pattern recognition are described and their corresponding computational models are presented. This is followed with a survey from the literature of AIS applied to pattern recognition. The chapter is concluded with a trade-off between AIS and artificial neural networks as pattern recognition paradigms.

AISIMAM – An Artificial immune system based intelligent multi agent model and its application to a mine detection problem

Abstract: Artificial Immune System (AIS) is a novel evolutionary paradigm inspired by the biological aspects of the immune system. The human immune system has motivated scientists and engineers for finding powerful information processing algorithms that has solved complex engineering tasks. This paper discusses two concepts. (a) The behavioral management of artificial intelligence (AI) namely the intelligent multi agent systems, (b) The evolutionary computation called the artificial immune system that imitates the biological theory called the immune system. The outcome of this research is an Artificial Immune System based Intelligent Multi Agent Model named AISIMAM that solves agent-based applications. The model is applied to a mine detection and diffusion problem and the results prove that AISIMAM has solved the problem successfully.

Artificial Immune System: Principle, Models, Analysis and Perspectives

Abstract: Drawing inspiration from the vertebrate immune system, a new research field of Artificial Immune System (AIS) is springing up. As a novel branch of computational intelligence, AIS has strong capabilities of pattern recognition, learning and associative memory, hence it is natural to view AIS as a powerful information processing and problem-solving paradigm in both the scientific and engineering fields. This paper intends to give a comprehensive overview of AIS based on a preliminary theoretical framework, which is started with the brief interpretative introduction of biological models of vertebrate immune system, then followed with some extracted bionic principles, viz. immune recognition, immune learning, immune memory, clone selection, diversity generation and maintenance etc. The mapping from natural immune system to AIS models is emphasized in this paper. As a result, some typical AIS based models and algorithms are discussed through classifications. It is the real engineering applications that draw the broad attention of computer scientists to recognize the great potential of AIS, hereby some important application fields as information security, pattern recognition, optimization, machine learning, data mining, robotics, diagnostics and cybernetics etc. are reviewed. Then based on the property analysis of AIS, some key problems in the state-of-the-art of AIS research are investigated, through which we hope to gain deep insight into AIS and suggest some new ideas that may be of value for AIS model development. Finally, some possible research directions of AIS are given by the authors in a further step as the summary of this paper, among which the application of AIS to evolutionary design is emphasized.

Makespan minimization on parallel processors: an immune-based approach

Abstract: This work deals with the problem of scheduling jobs to identical parallel processors with the goal of minimizing the completion time of the last processor to finish its execution (makespan). This problem is known to be NP-Hard. The algorithm proposed here is inspired by the immune systems of vertebrate animals. The advantage of combinatorial optimization algorithms based on artificial immune systems is the inherent ability to preserve a diverse set of near-optimal solutions along the search. The results produced by the method are compared with results of classical heuristics.

Immune, swarm, and evolutionary algorithms. Part I: basic models

Abstract: These two papers have three main aims. First (Part I), to review the general algorithms of immune, swarm and evolutionary systems. Second (Part II), to present a philosophical discussion about the similarities and differences between these paradigms, in terms of components, architecture, adaptation, interactions, and metaphors. Finally (Part II), to highlight the main features embodied in each approach, such that avenues for the creation of hybrid models can be suggested.

A parallel implementation of an artificial immune system to handle constraints in genetic algorithms: preliminary results

Abstract: We present a parallel version of a constraint-handling technique based on the artificial immune system. The proposed approach does not require penalty factors of any kind, it is relatively simple to implement and it is quite competitive with more sophisticated techniques. Additionally, when parallelized using an island scheme, the approach not only reduces its computational time, but it also improves the quality of the results produced.

Function optimization by the immune metaphor

Abstract: The main goal of the immune system is to protect an organism against pathogens. To be able to recognize unknown (i.e. never seen) pathogens, the immune system applies a number of methods allowing to maintain sucien t diversity of its receptors. The most important methods are clonal selection and suppression of ineectiv e receptors. In eect the immune system admits maturation anit y property: during its functioning it continuously improves its ability to recognize new types of pathogens. This idea had found many interesting computer-oriented applications. In this paper a simple and easy to implement algorithm for multi-modal as well as non-stationary functions optimization is proposed. It is based on clonal selection and cells suppression mechanisms. Empirical results conrming its usability for uni-, multi-modal and non-stationary functions optimization are presented, and a review of other immunity-based approaches is given.

An Novel Artificial Immune System Approach to Robust Data Mining.

Abstract: We introduce several enhancements to deal with some of the weaknesses of previous artificial immune system models. Then, we present a framework for the accomplishment of several classical data mining tasks, such as frequent itemset discovery and robust clustering, based on ideas inspired from the natural immune system coupled with soft computing. For instance, we implement an artificial immune system mimicking the body’s adaptive learning and defense mechanism in the face of invading biological agents, to monitor and learn the user activities on a Web site. Like the natural immune system, the strongest advantage of immune based learning compared to current approaches is expected to be its ease of adaptation to the dynamic environment that characterizes several applications. We use both synthetic spatial data and real Web usage data to illustrate the workings of this novel computational paradigm.

Pattern recognition by immunocomputing

Abstract: The authors had developed a rigorous mathematical approach, describing the operation of the immune system based on the models of proteins and immune networks. An immunocomputing approach is proposed as a computational basis for artificial immune systems. A further development of immunocomputing and its application to pattern recognition is considered herein. It is shown that intrusion detection in computer networks presents a possible implementation of immunocomputing.