Showing papers on "Artificial immune system published in 2005"

Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection

Abstract: Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of co-ordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

How Do We Evaluate Artificial Immune Systems

Abstract: The field of Artificial Immune Systems (AIS) concerns the study and development of computationally interesting abstractions of the immune system. This survey tracks the development of AIS since its inception, and then attempts to make an assessment of its usefulness, defined in terms of 'distinctiveness' and 'effectiveness.' In this paper, the standard types of AIS are examined—Negative Selection, Clonal Selection and Immune Networks—as well as a new breed of AIS, based on the immunological 'danger theory.' The paper concludes that all types of AIS largely satisfy the criteria outlined for being useful, but only two types of AIS satisfy both criteria with any certainty.

An artificial immune system approach with secondary response for misbehavior detection in mobile ad hoc networks

Abstract: In mobile ad hoc networks, nodes act both as terminals and information relays, and they participate in a common routing protocol, such as dynamic source routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. In this paper, we investigate the use of an artificial immune system (AIS) to detect node misbehavior in a mobile ad hoc network using DSR. The system is inspired by the natural immune system (IS) of vertebrates. Our goal is to build a system that, like its natural counterpart, automatically learns, and detects new misbehavior. We describe our solution for the classification task of the AIS; it employs negative selection and clonal selection, the algorithms for learning and adaptation used by the natural IS. We define how we map the natural IS concepts such as self, antigen, and antibody to a mobile ad hoc network and give the resulting algorithm for classifying nodes as misbehaving. We implemented the system in the network simulator Glomosim; we present detection results and discuss how the system parameters affect the performance of primary and secondary response. Further steps will extend the design by using an analogy to the innate system, danger signal, and memory cells.

An Artificial Immune System for Misbehavior Detection in Mobile Ad-Hoc Networks with Virtual Thymus, Clustering, Danger Signal and Memory Detectors

Abstract: Nodes that build a mobile ad-hoc network participate in a common routing protocol in order to provide multi-hop radio communication. Routing defines how control information is exchanged between nodes in order to find the paths between communication pairs, and how data packets are relayed. Such networks are vulnerable to routing misbehavior, due to faulty, selfish or malicious nodes. Misbehavior disrupts communication, or even makes it impossible in some cases. Misbehavior detection systems aim at removing this vulnerability. For this purpose, we use an Artificial Immune System (AIS) approach, i.e, an approach inspired by the human immune system (HIS). Our goal is to make an AIS that, analogously to its natural counterpart [16], automatically learns and detects new misbehavior, but becomes tolerant to previously unseen normal behavior. We achieve this goal by adding some new AIS concepts to those that already exist: (1) the virtual thymus, which provides a dynamic description of normal behavior in the system; (2) “clustering” is a decision making method that reduces the false-positive detection probability and minimizes the time until detection; (3) we apply the “danger signal” approach, that is recently proposed in AIS literature [5,6] as a way to obtain feedback from the protected system and use it for correct learning and finaldecisions making; (4) we use “memory detectors”, a standard AIS solution to achieve fast secondary response.

An immunity based network security risk estimation

Abstract: According to the relationship between the antibody concentration and the pathogen intrusion intensity, here we present an immunity-based model for the network security risk estimation (Insre). In Insre, the concepts and formal definitions of self, nonself, antibody, antigen and lymphocyte in the network security domain are given. Then the mathematical models of the self-tolerance, the clonal selection, the lifecycle of mature lymphocyte, immune memory and immune surveillance are established. Building upon the above models, a quantitative computation model for network security risk estimation, which is based on the calculation of antibody concentration, is thus presented. By using Insre, the types and intensity of network attacks, as well as the risk level of network security, can be calculated quantitatively and in real-time. Our theoretical analysis and experimental results show that Insre is a good solution to real-time risk evaluation for the network security.

Conceptual Frameworks for Artificial Immune Systems

Abstract: We propose that bio-inspired algorithms are best developed and analysed in the context of a multidisciplinary conceptual framework that provides for sophisticated biological models and well-founded analytical principles, and we outline such a framework here, in the context of Artificial Immune System (AIS) network models, and we discuss mathematical techniques for analysing the state dynamics of AIS. We further propose ways to unify several domains into a common meta-framework, in the context of AIS population models. We finally discuss a case study, and hint at the possibility of a novel instantiation of such a meta-framework, thereby allowing the building of a specific computational framework that is inspired by biology, but not restricted to any one particular biological domain.

The medical applications of attribute weighted artificial immune system (AWAIS): diagnosis of heart and diabetes diseases

Abstract: In our previous work, we had been proposed a new artificial immune system named as Attribute Weighted Artificial Immune System (AWAIS) to eliminate the negative effects of taking into account of all attributes in calculating Euclidean distance in shape-space representation which is used in many network-based Artificial Immune Systems (AISs). This system depends on the weighting attributes with respect to their importance degrees in class discrimination. These weights are then used in calculation of Euclidean distances. The performance analyses were conducted in the previous study by using machine learning benchmark datasets. In this study, the performance of AWAIS was investigated for real world problems. The used datasets were medical datasets consisting of Statlog Heart Disease and Pima Indian Diabetes datasets taken from University of California at Irvine (UCI) Machine Learning Repository. Classification accuracies for these datasets were obtained through using 10-fold cross validation method. AWAIS reached 82.59% classification accuracy for Statlog Heart Disease while it obtained a classification accuracy of 75.87% for Pima Indians Diabetes. These results are comparable with other classifiers and give promising performance to AWAIS for that kind of problems.

Towards a conceptual framework for innate immunity

Abstract: Innate immunity now occupies a central role in immunology. However, artificial immune system models have largely been inspired by adaptive not innate immunity. This paper reviews the biological principles and properties of innate immunity and, adopting a conceptual framework, asks how these can be incorporated into artificial models. The aim is to outline a meta-framework for models of innate immunity.

A Machine Learning Evaluation of an Artificial Immune System

Abstract: ARTIS is an artificial immune system framework which contains several adaptive mechanisms. LISYS is a version of ARTIS specialized for the problem of network intrusion detection. The adaptive mechanisms of LISYS are characterized in terms of their machine-learning counterparts, and a series of experiments is described, each of which isolates a different mechanism of LISYS and studies its contribution to the system's overall performance. The experiments were conducted on a new data set, which is more recent and realistic than earlier data sets. The network intrusion detection problem is challenging because it requires one-class learning in an on-line setting with concept drift. The experiments confirm earlier experimental results with LISYS, and they study in detail how LISYS achieves success on the new data set.

Handling constraints in global optimization using an artificial immune system

Abstract: In this paper, we present a study of the use of an artificial immune system (CLONALG) for solving constrained global optimization problems. As part of this study, we evaluate the performance of the algorithm both with binary encoding and with real-numbers encoding. Additionally, we also evaluate the impact of the mutation operator in the performance of the approach by comparing Cauchy and Gaussian mutations. Finally, we propose a new mutation operator which significantly improves the performance of CLONALG in constrained optimization.

Clonal selection with immune dominance and anergy based multiobjective optimization

Abstract: Based on the concept of Immunodominance and Antibody Clonal Selection Theory, we propose a new artificial immune system algorithm, Immune Dominance Clonal Multiobjective Algorithm (IDCMA). The influences of main parameters are analyzed empirically. The simulation comparisons among IDCMA, the Random-Weight Genetic Algorithm and the Strength Pareto Evolutionary Algorithm show that when low-dimensional multiobjective problems are concerned, IDCMA has the best performance in metrics such as Spacing and Coverage of Two Sets.

Fuzzy goal-programming model with an artificial immune system (AIS) approach for a machine tool selection and operation allocation problem in a flexible manufacturing system

Abstract: Some of the important planning problems that need realistic modelling and a quicker solution, especially in automated manufacturing systems, have recently assumed greater significance. In real-life industrial applications, the existing models considering deterministic situations fail as the true language adopted by foremen and technicians are fuzzy in nature. Thus, to map the situation on the shop floor to arrive at a real-time solution of this kind of tactical planning problem, it is essential to adopt fuzzy-based multi-objective goals so as to express the target desired by the management of business enterprises. This paper presents a fuzzy goal-programming approach to model the machine tool selection and operation allocation problem of flexible manufacturing systems. The model is optimized using an approach based on artificial immune systems and the results of the computational experiments are reported.

Multiobjective optimization by a modified artificial immune system algorithm

Abstract: The aim of this work is to propose and validate a new multiobjective optimization algorithm based on the emulation of the immune system behavior. The rationale of this work is that the artificial immune system has, in its elementary structure, the main features required by other multiobjective evolutionary algorithms described in literature. The proposed approach is compared with the NSGA2 algorithm, that is representative of the state-of-the-art in multiobjective optimization. Algorithms are tested versus three standard problems (unconstrained and constrained), and comparisons are carried out using three different metrics. Results show that the proposed approach have performances similar or better than those produced by NSGA2, and it can become a valid alternative to standard algorithms.

Immunity from spam: an analysis of an artificial immune system for junk email detection

Abstract: Despite attempts to legislate them out of existence, spam messages (junk email) continue to fill electronic mailboxes around the world. With spam senders adapting to each technical solution put on the market, adaptive solutions are being incorporated into new products. This paper undertakes an extended examination of the spam-detecting artificial immune system proposed in [1,2], focusing on comparison of scoring schemes, the effect of population size, and the libraries used to create the detectors.

On the appropriateness of negative selection defined over Hamming shape-space as a network intrusion detection system

Abstract: Artificial immune systems have become popular in recent years as a new approach for intrusion detection systems. Indeed, the (natural) immune system applies very effective mechanisms to protect the body against foreign intruders. We present empirical and theoretical arguments, that the artificial immune system negative selection principle, which is primarily used for network intrusion detection systems, has been copied to naively and is not appropriate and not applicable for network intrusion detection systems.

An immune based dynamic intrusion detection model

Abstract: With the dynamic description method for self and antigen, and the concept of dynamic immune tolerance for lymphocytes in network-security domain presented in this paper, a new immune based dynamic intrusion detection model (Idid) is proposed. In Idid, the dynamic models and the corresponding recursive equations of the lifecycle of mature lymphocytes, and the immune memory are built. Therefore, the problem of the dynamic description of self and nonself in computer immune systems is solved, and the defect of the low efficiency of mature lymphocyte generating in traditional computer immune systems is overcome. Simulations of this model are performed, and the comparison experiment results show that the proposed dynamic intrusion detection model has a better adaptability than the traditional methods.

A new classification method for breast cancer diagnosis: feature selection artificial immune recognition system (FS-AIRS)

Abstract: In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with a new approach, FS-AIRS (Feature Selection Artificial Immune Recognition System) algorithm that has an important place in classification systems and was developed depending on the Artificial Immune Systems. With this purpose, 683 data in the Wisconsin breast cancer dataset (WBCD) was used. In this study, differently from the studies in the literature related to this concept, firstly, the feature number of each data was reduced to 6 from 9 in the feature selection sub-program by means of forming rules related to the breast cancer data with the C4.5 decision tree algorithm. After separating the 683 data set with reduced feature number into training and test sets by 10 fold cross validation method in the second stage, the data set was classified in the third stage with AIRS and a quite satisfying result was obtained with respect to the classification accuracy compared to the other methods used for this classification problem.

Once More Unto the Breach: Towards Artificial Homeostasis?

Abstract: The field of biologically inspired computing has generated many novel, interesting and useful computational systems. None of these systems alone is capable of approaching the level of behaviour for which the artificial intelligence and robotics communities strive. We suggest that it is now time to move on to integrating a number of these approaches in a biologically justifiable way. To this end we present a conceptual framework which integrates artificial neural networks, artificial immune systems and a novel artificial endocrine system. The natural counterparts of these three components are usually assumed to be the principal actors in maintaining homeostasis within biological systems. This chapter proposes a system, which promises to capitalise on the self-organising properties of these artificial systems to yield artificially homeostatic systems. The components develop in a common environment and interact in ways which draw heavily on their biological counterparts for inspiration. A case study is presented, in which aspects of the nervous and endocrine systems are exploited to create a simple robot controller. Mechanisms for the moderation of system growth using an artificial immune system are also presented.

A comparative analysis of artificial immune network models

Abstract: This paper presents a review of different artificial immune network models, which have been published during the last years A general model of artificial immune network is presented, which provides a common notation that allows the comparison of different models A descriptive and comparative analysis is presented emphasizing similarities, differences and relationships between models Finally, some conclusions and suggestions for improving existent models are presented

Artificial homeostatic system: a novel approach

Abstract: Many researchers are developing frameworks inspired by natural, especially biological, systems to solve complex real-world problems. This work extends previous work in the field of biologically inspired computing, proposing an artificial endocrine system for autonomous robot navigation. Having intrinsic self-organizing behaviour, the novel artificial endocrine system can be applied to a wide range of problems, particularly those that involve decision making under changing environmental conditions, such as autonomous robot navigation. This work draws on “embodied cognitive science”, including the study of intelligence, adaptivity, homeostasis, and the dynamic aspects of cognition, in order to help lay down fundamental principles and techniques for a novel approach to more biologically plausible artificial homeostatic systems. Results from using the artificial endocrine system to control a simulated robot are presented.

Inspiration for the next generation of artificial immune systems

Abstract: In this conceptual paper, we consider the state of artificial immune system (AIS) design today, and the nature of the immune theories on which they are based. We highlight the disagreement amongst many immunologists regarding the concept of self–non-self discriminations in the immune system, and go on describe on such model that removes altogether the requirement for self–non-self discrimination. We then identify the possible inspiration ideas for AIS that can be gained from such new, and often radical, models of the immune system. Next, we outline a possible approach to designing AIS that are inspired by new immune theories, following a suitable methodology and selecting appropriate modelling tools. Lastly, we follow our approach and present an example of how the AIS designer might take inspiration from a specific property of a new immune theory. This example highlights our proposed method for inspiring the design of the next generation of AIS.

Two ways to grow tissue for artificial immune systems

Abstract: An immune system without tissue is like evolution without genes. Something very important is missing. Here we present the novel concept of tissue for artificial immune systems. Much like the genetic representation of genetic algorithms, tissue provides an interface between problem and immune algorithm. Two tissue-growing algorithms are presented with experimental results illustrating their abilities to dynamically cluster data and provide useful signals. The use of tissue to provide an innate immune response driving the adaptive response of conventional immune algorithms is then discussed.

Artificial immune system: methods for making and use

Abstract: The present invention relates to methods of constructing an integrated artificial immune system that comprises appropriate in vitro cellular and tissue constructs or their equivalents to mimic the normal tissues that interact with vaccines in mammals. The artificial immune system can be used to test the efficacy of vaccine candidates in vitro and thus, is useful to accelerate vaccine development and testing drug and chemical interaction with the immune system.

An artificial immune system approach to document clustering

Abstract: It has recently been shown that artificial immune systems (AIS) can be successfully used in many machine learning tasks. The aiNet, one such AIS algorithm exploiting the biologically-inspired features of the immune system, performs well on elementary clustering tasks. This paper proposes the use of the aiNet to more complex tasks of document clustering. Based on the immune network and affinity maturation principles, the aiNet performs an evolutionary process on the raw data, which removes data redundancy and retrieves good clustering results. Also, Principal Component Analysis is integrated into this method to reduce the time complexity. The results are compared with some classical document clustering methods - Hierachical Agglomerative Clustering and K-means.

The Clonal Selection Principle for In Silico and In Vitro Computing

Abstract: The chapter describes the theory of clonal selection and its usage in designing and implementing immunological algorithms for problem solving and learning. In detail, it presents various immune algorithms based on the clonal selection principle, analyzing computational time complexity, experimental results, similarities and differences. It introduces two paradigms to model immune algorithms: noisy channel, and Turing’s reaction-diffusion systems to build artificial immune systems for effective information processing and computing. The authors show how Libchaber DNA algorithm can be interpreted as an “in vitro” implementation of the clonal selection principle by means of molecular biology technology. These similarities witness the ubiquity of such a kind of information processing in nature and give evidence of the universality of the concept of computation. The authors’ intent is to provide a general framework that can be considered as a first core for in silico and in vitro computation based on the clonal selection theory. INTRODUCTION Living systems are dynamical and reside in water at 320 °K. The constituents are of small size. It is a stochastic world of large amplitude vibrations and very viscous friction; force is proportional to velocity. All relevant energies are between 1 to 20 kT, This chapter appears in the book, Recent Developments in Biologically Inspired Computing, edited by Leandro N. de Castro and Fernando J. Von Zuben. Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com IDEA GROUP PUBLISHING The Clonal Selection Principle for In Silico and In Vitro Computing 105 Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. and all processes are out of equilibrium. Such a world needs a strict organization to function as a precise dynamical system or natural automata. Von Neumann (1956) observed: “Natural automata are superior to artificial ones, they have power of self diagnosis and self repair... It is to be expected a close relation of self-reproduction to self repair.” We can define the biological Immune System (IS) as the globally distributed selfrepair system of host organisms. The IS has to assure recognition of each potentially harmful molecule or substance, generically called antigen, that can infect the host organism. The IS first recognizes an antigen as harmful or extraneous and then mounts a response to eliminate it. To detect an antigen, the IS activates a recognition process. In vertebrate living organisms, a complex machinery of cellular interactions and molecular productions accomplishes this task. One can see the biological IS as a complex adaptive system of cells and molecules, distributed spatially, which provides the host organism with a basic defense against antigens. Immunology is the scientific discipline that studies the protection of organisms from antigens and their response to antigens. The first line of defense against antigens is barrier tissues such as the skin, which prevent the antigens from entering the body. If, however, these barrier layers are penetrated, the body contains cells that respond rapidly to the presence of invaders. These cells include macrophages that engulf antigens and kill them without the need for antibodies. This form of immunity is the innate or non-specific IS that is continually ready to respond to invasions. Hence, immunity is the resistance to the onset of diseases after infection by harmful antigens. Immunology grew out of the simple observation that after recovering from a particular infectious disease, one would become immune to further cases of that disease, but not to other infectious diseases. A second line of defense is the specific or adaptive IS, which may take days to respond to a primary invasion (that is, infection by an antigen that has not hitherto been seen). In the specific IS we see the production of antibodies (soluble proteins that bind to foreign antigens) and cell-mediated responses in which specific cells recognize foreign pathogens and destroy them. In addition, the host organism develops immune responses against our own proteins (and other molecules) in autoimmunity and against its aberrant cells in tumor immunity. The IS can learn about the outside environment and adapt to it. It remembers not only what it has interacted with, but also how it responded to the environmental challenges. Thus, immunity is adaptive and specific: two desirable features to have in an effective and robust computational system. Computational immunology is the research field that attempts to reproduce in silicon the behavior of the biological IS (Brusic & Petrovsky, 2002; Nicosia, Castiglione, Motta & Mannella, 1999), using computational tools and methods. Artificial immune systems (AIS) represent a new field of natural computing that attempts to use theories, principles, and concepts of modern immunology to design IS-based applications in science and engineering (Dasgupta, 1999; De Castro & Timmis, 2002). A new emerging discipline, called immunocomputing (Tarakanov, Skormin & Sokolova, 2003), explores the principles of information processing that proteins and immune networks utilize in order to solve specific complex problems while protecting from antigens. Immunocomputing develops proper concepts and mathematical definitions of IS’s basic elements. Three main innovations are expected to emerge from 42 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the publisher's webpage: www.igi-global.com/chapter/clonal-selection-principle-silicovitro/28326

The application of antigenic search techniques to time series forecasting

Abstract: Time series have been a major topic of interest and analysis for hundreds of years, with forecasting a central problem. A large body of analysis techniques has been developed, particularly from methods in statistics and signal processing. Evolutionary techniques have only recently have been applied to time series problems. To date, applications of artificial immune system (AIS) techniques have been in the area of anomaly detection. In this paper we apply AIS techniques to the forecasting problem. We characterize a class of search algorithms we call antigenic search and show their ability to give a good forecast of next elements in series generated from Mackey-Glass and Lorenz equations.

An Immuno Control Framework for Decentralized Mechatronic Control

Abstract: The Immune System is a complex adaptive system containing many details and many exceptions to established rules. Exceptions such as the suppression effect that causes T-cells to develop reversible aggressive and tolerant behaviors create difficulties for the study of immunology but also give hints to how artificial immune systems may be designed.

Adaptive radius immune algorithm for data clustering

Abstract: Many algorithms perform data clustering by compressing the original data into a more compact and interpretable representation, which can be more easily inspected for the presence of clusters. This, however, can be a risky alternative, because the simplified representation may contain distortions mainly related to the density information present in the data, which can considerably act on the clustering results. In order to treat this deficiency, this paper proposes an Adaptive Radius Immune Algorithm (ARIA), which is capable of maximally preserving the density information after compression by implementing an antibody adaptive suppression radius that varies inversely with the local density in the space. ARIA is tested with both artificial and real world problems obtaining a better performance than the aiNet algorithm and showing that preserving the density information leads to refined clustering results.