Artificial Immune Systems and Their Applications Springer Berlin Heidelberg New York Barcelona HongKong London Milan Paris Singapore Tokyo Dipankar Dasgupta (Ed.)

Artificial Immune Systems and Their Applications

With 100 Figures and 15 TabIes

, Springer Editor: Dipankar Dasgupta University of Memphis Mathematical Sciences Department Memphis, TN 38152-6429, USA [email protected]

ACM Computing Classification (1998): F.l.l, F.2.2, 1.2, 1.6, EA, J.3

ISBN-13: 978-3-642-64174-9 e-ISBN-13: 978-3-642-59901-9 DOI: 10.1007/978-3-642-59901-9

Library of Congress Cataloging-in-Publication Data Artificial immune systems and their applications / [edited by] Dipankar Dasgupta p. cm. Includes bibliographical references and index. ISBN 3-540-64390-7 (alk. paper) 1. - Computer simulation. 2. .1. Dasgupta, D. (Dipankar), 1958- QRI82.2.C65A78 1998 616.07'9'OI13-dc21 98-35558 CIP

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Cover design: design & production GmbH, Heidelberg Typesetting: Camera ready copy from the editor using a Springer TEX macro package SPIN: 10675823 45/3142 - 543210 - Printed on acid-free paper Preface

The natural immune system is a complex adaptive system which efficiently employs several mechanisms for defense against foreign pathogens. The main role of the immune system is to recognize all cells (or moleeules ) within the body and categorize those cells as self or non-self. The non-self cells are further categorized in order to induce an appropriate type of defensive mechanism. From an information-processing perspective, the immune system is a highly parallel intelligent system. It uses , , and associative retrieval to solve recognition and classification tasks. In particular, it learns to recognize relevant patterns (antigenic peptide), memorize patterns that have been seen previously, and use combinatorics (within gene libraries) to con• struct pattern detectors (V-regions in antibody) efficiently for distinguishing between foreign and the body's own cells. Moreover, the identifica• tion of antigens is not done by a single recognizing set but rather a system level mutual recognition through -antibody re action as a network. So the overall behavior of the immune system is an emergent property of many local interactions. The natural immune system is a subject of great research interest be• cause of its importance, complexity and poorly understood alternative mech• anisms. However, its general features provide an excellent model of adaptive processes operating at the local level and of useful behavior emerging at the global level. There exist several theories (some are contradictory) to explain immunological phenomena and computer models to simulate various compo• nents of the immune system. There is also a growing number of intelligent methodologies (inspired by the immune system) toward real-world . These methods are labeled with different names - Artificial Immune Systems, Immunity-Based Systems, Immunological Computation, etc. The scope of this field includes (but is not limited to) the following:

* Computational methods based on Immunological Principles * Immunity-Based Cognitive Models * Artificial Immune Systems for * Immunity-Based Systems for Anomaly or Fault Detection * Immunity-Based Multi-Agent Systems VI Preface

* Immunity-Based Systems for Self-organization * Immunity-Based approach for Collective Intelligence * Immunity-Based Systems for Search and Optimization * The Immune System as Autonomous Decentralized System * Immunity-Based approach for Artificial Life * Immunity-Based Systems for Computer & Internet Security * The Immune System as a metaphor for Learning System * Immunological Computation for Data Mining * Artificial Immune Systems for Fraud Detection * Immunity-Based Systems in Image & Signal Processing

As the field is growing, researchers started to organize scientific meetings which can serve as a forum for presenting and disseminating current research activities in the field. The first international workshop on "Immunity-based Systems" was held in Japan on December 10, 1996. Subsequently, there was a special track on "Artificial Immune Systems and Their Applications", or• ganized by the editor of this book, at the IEEE International Conference on Systems, Man, and Cyberneties (SMC' 97), Orlando, October 12-15, 1997. A similar event on this topies is also planned to organize at SMC' 98 to be held at San Diego, California. It seems particularly significant at this point of time, to put together important works in an edited volume in order to provide new researchers with a more thorough and systematic account of this rapidly emerging field. We feel that Artificial Immune Systems will so on receive similar attention like other biologically-motivated approaches, such as genetic algorithms, neural networks, cellular automata, etc. This is the first book that focuses on immunological computation tech• niques and their applications in many areas including computer security, data mining, , fault detection, etc. Though the emphasis of the book is on the computational aspects of the immune system, biological models are also considered since they are important to understand the immunological mechanisms and derive computational algorithms. The book will be useful for academician, researchers and practitioners in any scientific discipline who are interested in the models and applications of immunity-based systems. This volume consists of three parts: introduction, models of artificial im• mune systems and applications. Various chapters emphasize in-depth analysis of various immune system models and their relation to information processing and problem solving. The chapter by Dasgupta in the introductory section covers important immunologieal principles and their computational aspects. It also provides an overview of immunity-based computational models and their applications in pattern recognition, fault detection and diagnosis, computer security, and others. Bersini's chapter describes the double plasticity of the immune network that allows the system to conduct its self-assertion role while being in con• stant shift according to the organic's ontogenie changes and in response to Preface VII the environmental coupling. The author illustrates three application areas where the endogenous double level of adaptability, weakly inspired by the double plasticity in immune networks, allows to learn rapidly a satisfactory solution. In part 11, first chapter argues that the immune system is composed of two distinct compartments, a Central Immune System (CIS) and a Periph• eral Immune System (PIS). The PIS is composed of clones and is appropriate for reactions to immunizing antigens, whereas the CIS is ap• propriate for body antigens. The chapter also reviews the second generation immune network and proposes a third generation network model with an ef• fort to establish a productive relationship between theory and experiments of the immune system. Segal and Bar-Or view the immune system as an autonomous decentral• ized system. They propose three different models of such distributed systems and make some useful comparisons between the immune system and other autonomous decentralized systems. The chapter by Chowdhury presents mathematical models for describing the population dynamics of the immunocompetent cells in a unified man• ner by incorporating intra-clonal as weH as inter-clonal interactions in both discrete and continuous formulation. Smith et al. argue that immunological memory belongs to the same class of associative as Kanerva's sparse distributed memory (SDM). They show the correspondence between Band T ceHs in the immune system and hard locations in a SDM. In particular, their work demonstrates that Band T cells perform a sparse coverage of aH possible antigens in the same way that hard locations perform a sparse coverage of aH possible addresses in a SDM. Next two chapters in this part provide more biological insight of the hu• man immune system. In particular, in Tan and Xiang's chapter astate space model is developed to estimate and predict the number of free HIV and T ceHs in HIV-infected individuals using the Kalman filter method. Their mod• els express HIV pathogenesis in terms of stochastic differential equations. Based on observed RNA virus co pies over time, their models are validated by comparing the Kalman filter estimates of the RN A virus copies with the observed ones from patient data. According to the authors, these models may be useful for monitoring the dynamic behavior of the HIV process at the cel• lular level in HIV-infected individuals and for assessing the efficiencies and usefulness of the anti-viral treatment. The chapter on Modeling the Effects of Prior Infection on Vaccine Efficacy by Smith et al. provides computer simulations of the vertebrate humoral immune system to study the effects of prior infection on vaccine efficacy. The authors show that the effects of cross-reactive memory and original antigenic sin in the context of three antigens, and investigated how they can lead to VIII Preface

vaccine failure. They feel that this work has applications to understanding vaccination against viruses such as influenza that are mutating continually. In part III, the contribution by Hunt et al. describes a machine learning system based on metaphors taken from the immune system. They illustrate the current version of this Artificial Immune System (AIS), known as Jisys. The Jisys system can learn patterns in the data by incorporating existing domain knowledge explicitly within the pattern recognition and learning pro• cesses. The chapter illustrates the application of the Jisys system in detecting patterns in mortgage fraud data. The authors argue that the Jisys system is unique in nature and can address problems in a wide range of real world applications. Watanabe et al. construct a decentralized behavior arbitration mechanism inspired by the biological immune system, and confirm the validity of the proposed system through some experiments. In particular, they experiment with a garbage-collecting problem of autonomous mobile robot that takes into account the concept of self-sufficiency. The chapter on parallel search for multi-modal function optimization pro• poses an immune algorithm which can accommodate both diversity and learn• ing. The proposed algorithm is shown to be effective for searching for a set of global solutions as well as local solutions. KrishnaKumar and Neidhoefer developed immunized computational sys• tems that combine apriori knowledge with the adapting capabilities of im• mune systems to provide a powerful alternative to currently available tech• niques for intelligent control. They apply this immunized adaptive critics to a flight path generator for level 2, non-linear, full-envelope, intelligent aircraft control problem. The chapter by Kaphart et al. describe an immune system for comput• ers that senses the presence of a previously unknown pathogen, and within minutes automatically derives and deploys a prescription for detecting and removing it. According to the authors, this system is being integrated with a commercial anti-virus product, IBM AntiVirus. Dasgupta and Forrest develop an anomaly detection algorithm inspired by the immune system. This approach collects knowledge about the normal behavior of a system (or a process) from historical data sets, and generates a set of detectors that probabilistically notice any deviation from the normal behavior of the system. They experimented with a number of time series data and report results to illustrate the performance of the proposed detection algorithm. The last chapter (by Fukuda et al.) describes a framework for an au• tonomous distributed system. This is a multi-agent management system which they applied for decision making in production line of a semiconductor plant. Editing a book which covers interdisciplinary topics is a very difficult and time consuming project. The book could not have been successfully accom• plished without support and constructive feedback from the contributors. I Preface IX would like to thank all the contributors for their effort, for reviewing each others' work and for providing feedback on their own chapters. The comple• tion of this edited volume is accompanied by a great personal tragedy, as my father died of cancer in February, 1998. My sincere gratitude to the executive editor of Springer-Verlag, Hans Wössner, for his help throughout the project. This document has been pre• pared using the LATEX word processing system. I would like to thank Frank Holzwarth, Jacqueline Lenz and Gabi Fischer for their help in formatting the final manuscript of the book. I also like to thank my wife, Geeta for helping me with typing and encouraging in editing this volume.

August 1998 Dipankar Dasgupta Memphis, USA. Table of Contents

Part I. Introduction An Overview of Artificial Immune Systems and Their Applications ...... 3 Dipankar Dasgupta 1 Introduction...... 3 2 Computational Aspects of the Immune System ...... 5 3 The Nervous System and the Immune System...... 6 4 Immune System Based Models...... 7 5 Some Applications of Artificial Immune Systems ...... 12 6 Summary...... 17 References ...... 18

The Endogenous Double Plasticity of the Immune Network and the Inspiration to be Drawn for Engineering Artifacts . . .. 22 Hugues Bersini 1 Introduction...... 22 2 An Elementary Immune Network and the Basic Principles to be Obeyed by a Double Plastic Adaptive System...... 28 3 The Endogenous Double Plasticity in Neural Net Classifiers ...... 30 4 The Endogenous Double Plasticity in Autonomous Agents Learning by Reinforcement ...... 34 5 The Endogenous Double Plasticity for the Control of Chaos ...... 38 6 Conclusions...... 40 References ...... 41

Part 11. Artificial Immune Systems: Modeling & Simulation The Central and the Peripheral Immune Systems: What is the Relationship? ...... 47 lohn Stewart, lorge Carneiro 1 Introduction...... 47 2 Second Generation Network Models...... 48 XII Table of Contents

3 An Immune Network Incorporating B- Co-operation ...... 55 4 Concluding Remarks ...... 59 References ...... 61

Immunology Viewed as the Study of an Autonomous Decentralized System...... 65 Lee A. Segel, Ruth Lev Bar-Or 1 Introduction...... 65 2 A Nano-course in Immunology ...... 65 3 Overall Characterization of the Immune System ...... 67 4 Postulating a Role for Feedback...... 67 5 Optimizing Effector Performance...... 69 6 Optimizing Effector Choice ...... 74 7 The Importance of Geography ...... 79 8 Communication ...... 80 9 ABrief Comparison to Some Other Approaches to Decentralized Systems ...... 80 10 Overview ...... 83 References ...... 86

Immune Network: An Example of Complex Adaptive Systems 89 Debashish Chowdhury 1 Introduction...... 89 2 A Brief Summary of Experimental Phenomena to be Modelled Theoretically ...... 90 3 and Its Mathematical Modelling ...... 92 4 Beyond Clonal Selection; Immune Network ...... 98 5 Summary and Conclusion ...... 101 References ...... 102

Immunological Memory is Associative ...... 105 Derek J. Smith, Stephanie Forrest, Alan S. Perelson 1 Introduction ...... 105 2 Immunological Memory ...... 106 3 Sparse Distributed Memory (SDM) ...... 108 4 Correspondence between Immunological Memory and SDM ...... 109 5 Aspects of Associative Recall in the Immune Response...... 111 6 Summary ...... 112 References ...... 112 Estimating and Predicting the N umber of Free HIV and T Cells by Nonlinear KaIman Filter ...... 115 Wai- Yuan Tan, Zhihua Xiang 1 Introduction ...... 115 2 A Stochastic Model of the HIV Pathogenesis ...... 116 3 AState Space Model for the HIV Pathogenesis ...... 123 Table of Contents XIII

4 An Illustrative Example ...... 130 5 Some Monte Carlo Studies ...... 133 6 Conclusion and Discussion ...... 135 References ...... 138

Modeling the Effects of Prior Infection on Vaccine Efficacy ... 144 Derek l. Smith, Stephanie Forrest, David H. Ackley, Alan S. Perelson 1 Introduction ...... 144 2 Materials and Methods ...... 145 3 Results and Discussion ...... 148 References ...... 152

Part III. Artificial Immune Systems: Applications

Jisys: The Development of an Artificial Immune System for Real World Applications ...... 157 lohn Hunt, lon Timmis, Denise Cooke, Mark Neal, Clive King 1 Introduction ...... 157 2 Research into ISYS ...... 158 3 The lISYS System ...... 163 4 Jisys System Structure ...... 175 5 The Mortgage Fraud Application ...... 177 6 Analysis of lISYS ...... 179 7 Comparison with Related Work ...... 180 8 Future Work ...... 181 9 Conclusions ...... 184 References ...... 184 Decentralized Behavior Arbitration Mechanism for Autonomous Mobile Robot Using Immune Network ...... 187 Yuji Watanabe, Akio Ishiguro, Yoshiki Uchikawa 1 Introduction ...... 187 2 Biological Immune System ...... 189 3 Proposed Behavior Arbitration Mechanism Based on the Immune System ...... 191 4 Adaptation Mechanisms ...... 197 5 Conclusions and Further Work ...... 206 References ...... 207

Parallel Search for Multi-Modal Function Optimization with Diversity and Learning of Immune Algorithm ...... 210 Toyoo Fukuda, Kazuyuki Mori, Makoto Tsukiyama 1 Introduction ...... 210 2 Immune Algorithm ...... 211 3 Experiments and Implementation Details ...... 216 XIV Table of Contents

4 Conclusion ...... 219 References ...... 219 Immunized Adaptive Critic for an Autonomous Aircraft Control Application ...... 221 Kalmanje KrishnaKumar, lames Neidhoefer 1 Introduction ...... 221 2 Levels of Intelligent Control ...... 222 3 The Autonomous Aircraft Control Problem ...... 224 4 Immunized Computational Systems ...... 227 5 Immunized Adaptive Critics ...... 231 6 Conclusion ...... 237 References ...... 240 Blueprint for a Computer Immune System ...... 242 lefJrey O. Kephart, Gregory B. Sorkin, Morton Swimmer, Steve R. White 1 Introduction...... 242 2 Requirements for a Computer Immune System ...... 244 3 Implementing an Immune System for Cyberspace ...... 246 4 Evaluation and Final Remarks ...... 256 References ...... 259

An Anomaly Detection Algorithm Inspired by the Immune System ...... 262 Dipankar Dasgupta, Stephanie Forrest 1 Introduction ...... 262 2 A Negative Selection Algorithm ...... 263 3 Anomaly Detection...... 264 4 Experiments ...... 267 5 Conclusions ...... 273 References ...... 275 Immunity-Based Management System for a Semiconductor Production Line ...... 278 Toyoo Fukuda, Kazuyuki Mori, and Makoto Tsukiyama 1 Introduction ...... 278 2 Problem of Semiconductor Production System ...... 279 3 Immunity-Based System and Multi-Agent Nets ...... 281 4 Conclusion ...... 287 References ...... 288

Indexed Bibliography ...... 291 Author Index ...... 303 Subject Index ...... 304 List of Contributors

David H. Ackley Dipankar Dasgupta Department of Computer Science Department of Mathematical Sciences The University of New Mexico The University of Memphis Albuquerque, NM 87131, USA Memphis, TN 38152-6429, USA [email protected] [email protected]

Ruth Lev Bar-Or Department of Mathematics Stephanie Forrest and Computer Science Department of Computer Science Weizmann Institute The University of New Mexico Rehovot, Israel Albuquerque, NM 87131, USA [email protected] Hugues Bersini IRIDIA, CP 194/6 Toyoo Fukuda Universite Libre de Bruxelles School of Policy Studies 50, av. FrankIin Roosevelt Kwansei Gakuin University 1050 Bruxelles, Belgium 2-1 Gakuen Sanda [email protected] Hyogo 669-13, Japan Jorge Carneiro [email protected] Theoretical Biology & Bioinformatics Padualaan 8 John Hunt 3584 CH Utrecht Department of Computer Science The Netherlands University of Wales [email protected] Aberystwyth Penglais, Aberystwyth Debashish Chowdhury Dyfed, SY23 3DB, UK Department of Physics [email protected]. uk Indian Institute of Technology Kanpur 208016, India [email protected] Akio Ishiguro Department of Computational Denise Cooke Science and Engineering Department of Computer Science Graduate School of Engineering University of Wales, Aberystwyth Nagoya University Penglais, Aberystwyth Furo-cho, Chikusa-ku Ceredigion, SY23 3DB, UK Nagoya 464-01, Japan [email protected] [email protected] XVI List of Contributors

Jeffrey O. Kephart Lee A. Segel IBM Thomas J. Watson Department of Mathematics Research Center and Computer Science 30 Saw Mill River Rd. Weizmann Institute, Rehovot, Israel Hawthorne, NY 10532, USA [email protected] [email protected] Derek Smith Clive King Department of Computer Science Department of Computer Science University of New Mexico University of Wales, Aberystwyth Albuquerque, NM 87131, USA Penglais, Aberystwyth [email protected] Ceredigion, SY23 3DB, UK [email protected]. uk Gregory B. Sorkin Kalmanje KrishnaK umar IBM Thomas J. Watson Department of Aerospace Research Center Engineering and Mechanics P.O. Box 704,Yorktown Heights The University of Alabama NY 10598, USA Tuscaloosa, AL 35487-0280, USA [email protected] [email protected] J ohn Stewart Kazuyuki Mori COSTECH, Departement Technologie Industrial Electronics and et Sciences de I'Homme Systems Laboratory Universite de Technologie de Compiegne Mitsubishi Electric Corporation BP 60649, F-60206 Amagasaki, Hyogo 661, Japan F -60206 Compiegne cedex, France [email protected]/co.co.jp J ohn. [email protected]

Mark Neal Morton Swimmer Department of Computer Science IBM Thomas J. Watson University of Wales, Aberystwyth Research Center Penglais, Aberystwyth P.O. Box 704,Yorktown Heights Ceredigion, SY23 3DB, UK NY 10598, USA [email protected]. uk [email protected]

James Neidhoefer Wai-Yuan Tan Department of Aerospace Department of Mathematical Sciences Engineering and Mechanics The University of Memphis The University of Alabama Memphis, TN 38152-6429, USA Tuscaloosa, AL 35487-0280, USA [email protected] [email protected] Jonathan Timmis Alan S. Perelson Department of Computer Science Theoretical Division University of Wales, Aberystwyth Los Alamos National Laboratory Penglais, Aberystwyth Los Alamos, NM 87545, USA Ceredigion, SY23 3DB, UK [email protected] [email protected] List of Contributors XVII

Makoto Tsukiyama Steve R. White Industrial Electronics IBM Thomas J. Watson & Systems Laboratory Research Center Mitsubishi Electric Corporation 30 Saw Mill River Rd. Amagasaki, Hyogo 661, Japan Hawthorne, NY 10532, USA [email protected] [email protected]

Yoshiki U chikawa Zhihua Xiang Department of Computational Department of Mathematical Sciences Science and Engineering The University of Memphis Graduate School of Engineering Memphis, TN 38152-6429, USA Nagoya University, Furo-cho [email protected] Chikusa-ku Nagoya 464-8603, Japan [email protected]

Yuji Watanabe Department of Information Electronics Graduate School of Engineering Nagoya University, Furo-cho Chikusa-ku Nagoya 464-8603, Japan [email protected]