A Review of Artificial Immune System Based Security Frameworks for MANET <s> Artificial Immune Systems <s> Abstract The immune system is capable of learning, memory, and pattern recognition. By employing genetic operators on a time scale fast enough to observe experimentally, the immune system is able to recognize novel shapes without preprogramming. Here we describe a dynamical model for the immune system that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer. This model has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system. We demonstrate that simple versions of the classifier system can be cast as a nonlinear dynamical system, and explore the analogy between the immune and classifier systems in detail. Through this comparison we hope to gain insight into the way they perform specific tasks, and to suggest new approaches that might be of value in learning systems. <s> BIB001 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Artificial Immune Systems <s> Based on the strong analogy between neural networks and distributed diagnosis models, diagnostic algorithms are presented which are similar to the learning algorithm used in neural networks. Diagnostic implications of convergence theorems proved by the Lyapunov function are also discussed. Regarding diagnosis process as a recalling process in the associative memory, a diagnostic method of associative diagnosis is also presented. A good guess of diagnosis is given as a key to recalling the correct diagnosis. The authors regard the distributed diagnosis as an immune network model, a novel PDP (parallel distributed processing) model. This models the recognition capability emergent from cooperative recognition of interconnected units <s> BIB002 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Artificial Immune Systems <s> The problem of protecting computer systems can be viewed generally as the problem of learning to distinguish self from other. The authors describe a method for change detection which is based on the generation of T cells in the immune system. Mathematical analysis reveals computational costs of the system, and preliminary experiments illustrate how the method might be applied to the problem of computer viruses. > <s> BIB003 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Artificial Immune Systems <s> We present a simplified view of those parts of the human immune system which can be used to provide the basis for a data analysis tool. The motivation for and reasoning behind such a model is given and the desire for a 'transparent' model and meaningful visualization and interpretation techniques is noted. A minimalist formulation of an artificial immune system and some of its behaviour is described. A simple implementation and a suitable visualization technique are demonstrated using some trivial data and the famous 'iris' data set. <s> BIB004 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Artificial Immune Systems <s> 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. <s> BIB005 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Artificial Immune Systems <s> From the Publisher: ::: "Immunocomputing: Principles and Applications presents a needed bridge between immunology and computer engineering, demonstrating how quantitative advancements in immunology form the basis for a new computing paradigm. After first building a rigorous mathematical and theoretical framework for the technology, the book then develops an immunocomputing (IC) approach to modeling natural and technical systems, describes several IC applications and a hardware immunochip as the core of a emerging immunocomputer devices." This thorough introduction to immunocomputing is a valuable resource for experts in computer science, artificial intelligence, and biomolecular computing who would like to explore the principles of IC, as well as for immunologists seeking to further quantify their research. It will also assist multidisciplinary researchers in mutually enhancing computer science and immunology methods. <s> BIB006 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Artificial Immune Systems <s> 1 Introduction.- 2 Toward a Systems Science for Biological Systems.- 3 The Immune System as an Information System.- 4 Defining Immunity-Based Systems.- 5 A Self-Organizing Network Based on the Concept of the Immune Network.- 6 Sensor Networks Using the Self-Organizing Network.- 7 A Multiagent Framework Learned from the Immune System.- 8 An Application of the Immune Algorithm with an Agent Framework.- 9 Information Flow, Biological Field, and Autonomous Distributed Systems.- 10 The Immune System as a Self-Defining Process.- 11 Conclusions.- References. <s> BIB007
The field of AIS has achieved importance as a branch of Computational Intelligence since its inception in the 1990s. The four major AIS algorithms on which research is centered are: 1) Negative Selection Algorithm (NSA); 2) Artificial Immune Networks; 3) Clonal Selection Algorithm; and 4) Danger Theory and Dendritic Cell Algorithms. AI brings together the disciplines of Immunology, Computer Science and Engineering. Over the past decade research into the Immune System has gained popularity as a vehicle for novel solutions to complex issues. The highly distributed, adaptive nature of the immune system includes capabilities such as learning, memory and pattern recognition which are solid foundations for an artificial equivalent. AIS outcomes require both integration of immunology and engineering to transform the complex evolved mechanisms found in the HIS. Forrest et al. BIB003 proposed a negative selection method to distinguish self from non-self, based on the generation of T-cells. This approach was applied to the problem of virus detection in a computer and raised the profile of negative selection approaches. Following the work by Forrest et al. variations of the negative selection algorithm have been developed with the essential properties of the original negative selection algorithm remaining. Artificial Immune Networks (AINs) are another popular AIS approach that was based on Farmer et al.'s immune network model BIB001 and an early immune network algorithm was designed by Ishida BIB002 . Timmis et al. BIB004 redefined the artificial immune network. Castro et al.  proposed the Clonal Selection Algorithm (CLONALG) which is based on a clonal selection principle processes similar to those found in Genetic Algorithms including clone, mutation and reselection. The AIS community has produced a multifaceted set of immune inspired algorithms in order to solve computational as well as real world problems. Castro and Timmis BIB005 provided a detailed analysis of the Immune System and a presentation of current AIS algorithms. Tarakanov et al. BIB006 provided an insight into the mathematical basis of immunocomputing. Ishida BIB007 reviewed immune network models and highlighted the benefits of each approach.