Artificial Immune Systems: Third International Conference, ICARIS 2004, Catania, Sicily, Italy, September 13-16, 2004, Proceedings
March 11, 2010 by BioinformaticsDirectory.com
Product Description
This book constitutes the refereed proceedings of the Third International Conference on Artificial Immune Systems, ICARIS 2004, held in Catania, Sicily, Italy in September 2004.
The 34 revised full papers presented were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections on applications of artificial immune systems; conceptual, formal, and theoretical frameworks; artificial immune systems for robotics; emerging metaphors; immunoinformatics; theoretical and experimental studies; future applications; networks; modeling; and distinguishing properties of artificial immune systems.
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The book in general provides meaningful information about the topic. It give you a good start to design your own AIS based system. I recommend it for grad students in computer science.
Rating: 5 / 5
I recently started my Thesis on Artificial Immune Systems, and this was the first place I looked. This book gave me the foundation that I needed to start my own AIS. I was a little disappointed with the fact that the book is outdated, and a lot of the sources that the authors use are no longer available, but if you know how search for people on the Internet then it isn’t a problem.
The first couple of chapters go through the history and the functionality of the vertebrate immune system. This is obviously important when it comes to creating your own AIS. However, if you already did your research on the immune system then it is possible to just skip these chapters. The next couple chapters explain how it is possible to convert biological functions to computer code. The book does bring up a lot of immune-inspired algorithms to the table, and it explains how they work. Some more chapters explain some examples of how one could use an AIS. For example, there are some explanations on how an AIS can be used for an intrusion detection system or optimization. There are many more, but I won’t list them here. The later chapters didn’t really pertain to me, so I didn’t read them thoroughly. The chapters bring up the future of AIS and it’s possibilities. Overall, I would highly reccommend this book to anyone who is interested in either AIS in general, or if you are starting out a project pertaining to an AIS.
Some things to think about when buying this book:
Yes, this book is the perfect place to start if you know nothing about AIS.
Yes, this book does offer some pseudocode algorithms, and they do help.
Not every chapter of this book is necessary to read.
Yes this book goes over immune system algorithms.
No, this book will not code your immune system if that is what you are thinking.
Rating: 5 / 5
Bio-inspired computing has taken the world by storm in the last few decades, going by the names of neural networks, genetic algorithms, evolutionary programming, and swarm intelligence. Another one has arisen has appeared in the last 15 years or so, is inspired by the biology of the immune system, and is the subject of this book. The authors of the book are aware that the approach is novel, but do a good job of presenting the field to newcomers (like myself), who want to know what it is all about and if it indeed has useful applications. They discuss their own work in the area and that of others, and extensive references are given for further reading.
After a short introduction to the subject in chapter 1, the authors move on to a description of the biological immune system in chapter 2. They stress the need for understanding the mechanisms that regulate the adaptive immune response, so as to be able to control the transformation of an immune response from an “aggressive” to a “benign” state. The authors explain the difference between the “innate” immune system and the “adaptive” immune system. As the name implies, the adaptive immune response is a kind of “learning” ability that allows the immune system to improve itself as antigens are encountered. The innate immune response though remains constant along the lifetime of the organism. A short description of the T-cells and B-cells is given, some of which can differentiate into “memory cells” that remain circulating in the body and protect against a given antigen. Particularly interesting is the role of “pattern recognition receptors” that recognize molecular patterns associated with pathogens. The clonal selection theory of the adaptive immune system, along with the somewhat controversial immune network theory.
Chapter 3 is an overview of how to to actually create an artificial immune system (AIS). The emphasize that anything deemed controversial in the biological framework need not be when viewed from a computational perspective, such as the immune network theory. Biology is used for the inspiration of the computational models, and as such they need not reflect entirely what is true in the biological case. They also emphasize that the various attempts to simulate the immune system on computers are not examples of an AIS. Also, an AIS is more than just a pattern recognition algorithm, even though it must employ this in its use. To give a framework for an AIS, the authors employ a model of immune cells and molecules called a “shape-space”. In this shape space one models the affinity of the “molecules” via a metric, which the authors eventually choose to be the Hamming metric. They then give an overview of various algorithms for modeling the immune system, such as bone marrow, thymus, and immune network models, in addition to clonal selection algorithms. For those readers familiar with dynamical systems, the immune network models are very interesting, due to the use of differential equations, and also the fact that such in immune network models the immune system is performing even in the absence of external stimuli.
Chapter 4 gives a survey of artificial immune systems, such as spectra recognition for chemical reactions, infectious disease surveillance, analysis of medical data, and computational security. The latter was of particular importance to me, so I read the discussion and the references with more attention than other parts of the book. The issue with the approaches for network intrusion detection and virus detection lie mostly in the performance of the network. Agents that are cleverly designed may form a very accurate way of detecting this malicious behavior, but their deployment on a network may degrade the its performance considerably.
I did not read chapters 5 and 6 so I will omit their review.
In chapter 7, the authors discuss various case studies in artificial immune systems that shed more light on the examples of Chapter 4. The computer network security application is discussed again, and a low number of false positives is shown to follow after the artificial immune system is simulated. However, the performance of the network is not pointed out by the authors. The authors also give more details on the application of artificial immune systems to data analysis and optimization. The discussion is interesting, but it is still an open question as to whether this approach is indeed better than other ones in optimization theory, i.e. how does the immune approach compare with the “free-lunch” theorems so often quoted in optimization theory? The authors do make a brief comparison of their optimization algorithm with evolution strategies, and this is somewhat helpful to those who are familiar with the latter.
The last chapter of the book looks to future applications of artificial immune systems, and in its connection with learning paradigms in artificial intelligence. The authors are open-minded about the future of AIS but also subject it to critical analysis.
The book motivated me to investigate the use of AIS more fully, and to begin thinking about possible applications, such as 1. Event correlation in networks. 2. Network routing: Routes that are inefficient are viewed as “antigens”, and the network immune system will then cure the system of these routes, meaning that it will remember them as being antigens up to some practical time scale. The routing scheme in place will not implement these routes within this time frame. 3. The TCP/IP protocol in the context of the immune network theory where reliable connections are based on the epitope/paratope recognition capability. Any emergent properties of the network overlaid with the TCP/IP protocol such as learning, memory, and self-tolerance could be studied by viewing the packet network as an immune network. 4. Network QoS, with packets marked as low priority viewed as temporary antigens. 5. Using the function optimization capabilities of AIS do calculate the effective bandwidth of ATM networks. 6. Data analysis, particularly in the construction of algorithms to distinguish chaos from noise.
Rating: 3 / 5