Summary. The BEYOND architecture applies biological principles and mechanisms to design network applications that autonomously adapt to dynamic environmental changes in the network. In BEYOND, each network application consists of distributed software agents, analogous to a bee colony (application) consisting of multiple bees (agents). Each agent provides a particular functionality of a network application, and implements biological behaviors such as energy exchange, migration, reproduction and replication. This paper describes two key components in BEYOND: (1) a self-regulatory and evolutionary adaptation mechanism for agents, called iNet, and (2) an agent development environment, called BEYONDwork. iNet is designed after the mechanisms behind how the immune system detects antigens (e.g., viruses) and produces antibodies to eliminate them. It models a set of environment conditions (e.g., network traffic) as an antigen and an agent behavior (e.g., migration) as an antibody. iNet allows each agent to autonomously sense its surrounding environment conditions (i.e., antigens) and adaptively invoke a behavior (i.e., antibody) suitable for the conditions. In iNet, a configuration of antibodies is encoded as a gene. Agents evolve their antibodies so that they can adapt to unexpected environmental changes. iNet also allows each agent to detect its own deficiencies to detect antigen invasions (i.e., environmental changes) and regulate its policy for antigen detection. Simulation results show that agents adapt to changing network environments by self-regulating their antigen detection and evolving their antibodies through generations. BEYONDwork provides visual and textual languages to design agents in an intuitive manner. 1

Stochastic Diffusion Search (sds) was introduced by Bishop (1989a) as an algorithm to solve pattern matching problems. It relies on many concurrent partial evaluations of candidate solutions by a population of agents and communication between those agents to locate the optimal match to a target pattern in a search space. In subsequent research, several variations on the original algorithmic formulation were proposed. It also became evident that its main principles – partial evaluation and communication between agents – can be employed to problems outside the pattern matching domain. The primary aim of this dissertation is to develop these expansive views further: sds is proposed as a metaheuristic, a generic heuristic procedure for solving problems through search. Furthermore, it is proposed as a challenge to the dominant metaphor in computer science: sequential computation. The thesis proceeds in a structured way by first considering all questions that can be asked about a heuristic procedure like sds: questions of a foundational nature, questions pertaining to mathematical analysis, questions about application domains and questions about physical implementation. It is to the foundational issues that most attention is devoted. Analogies with selective processes in natural and social systems are investigated, as well as analogies with other metaheuristic techniques from artificial intelligence. An attempt is made to categorise potential variants, and to establish what kind of problems sds would be the optimal problem-solving method for. The work aims to provide an expanded but structured understanding of sds, to give guidelines for future work, and to establish how progress in other scientific disciplines can be of use in the study of sds, and vice versa. Preface All sciences characterise the essential nature of the systems they study. These characterisations are invariably qualitative in nature, for they set the terms with which more detailed knowledge can be developed. A. Newell and H. Simon (Newell and Simon, 1976) Cybernetics is the science of defensible metaphors.

In this thesis we show how computers can protect themselves from di#erent forms of attacks, mis-configurations, and program errors. The work is inspired by the immune system and in a similar vein to the immune system our system learns how to distinguish self from nonself. The learning is done on a system call level and profiles are constructed for the analysed programs. The scheduler then decides how much processing time each process should have according to how "normal" the program behaves. Hence, this system can be seen as a homeostatic feedback loop where the analysis of the system calls is the sensor and the scheduler the actuator that tries to maintain a stable environment. The system is implemented as a couple of modules to the Linux kernel and analyses each system call that is made by programs added to the system. To learn and analyse profiles of the system calls we have tried three di#erent methods, a table lookup method, a feedforward neural network, and an Elman recurrent neural network. Experiments show that this system can detect several methods of intrusion including bu#er over-flow attacks, format string attacks, and Trojan code. Contents 1

To explain the essential features such as sufficient diversity, discrimination of self and non-self, and also long-lasting immunologic memory of adaptive immune responses, Burnet and Talmage developed the clonal selection theory. In their model, only the high affinity immune cells are selected to proliferate. Those cells with low affinity must be efficiently deleted or be set as inactive. However, recent results suggest that low affinity cells would survive occasionally by altering their receptors. In this paper, in addition to combine receptor editing with clonal selection, a self-crossover operator is also implemented to improve algorithm's performance. Simulation on traveling salesman problems shows that this novel algorithm provides a better performance compared to the classical clonal selection algorithm.

algorithm on independent attributes to improve the performance of CLONALG Dr. S.Chitra 1, B.MadhuSudhanan 2,DR.M.Rajaram 3, DR.S.N.Sivanandham 4 It is a popularly held belief that preprocessing of data generally improves the classification efficiency of data mining algorithms. We study the effects of preprocess by utilizing an algorithm to cluster points in a data set based upon each attribute independently, resulting in additional information about the data points with respect to each of its dimensions. Noise, data boundaries are identified and the cleaned data subset is used to study the performance of CLONALG data mining algorithm against unprocessed dataset. I. ARTIFICIAL IMMUNE SYSTEMS

Abstract: This study presents a new artificial immune system for classification. It was named discrimination-based artificial immune system (DAIS) and was based on the principle of self and non-self discrimination by T cells in the human immune system. Ability of a natural immune system to distinguish between self and non-self molecules was applicable for classification in a way that one class was distinguished from others. We model this and the mechanism of the education in a thymus for classification. Especially, we introduce the method to decide the recognition distance threshold of the artificial lymphocyte, as the negative selection algorithm. We apply DAIS to real world datasets and show its performance to be comparable to that of other classifier systems. We conclude that this modeling was appropriate and DAIS was a useful classifier.

This thesis investigates the application of principles inspired from the development of immune memory to the problem of unknown motif detection in time series data. Motifs represent repeating patterns in the underlying data. As human beings we naturally seek patterns or motifs in data in order to understand that information. Motifs indicate high level properties of the data, summarising the information in a compact, intuitive and meaningful manner. Motifs help identify relationships in the data and they can aid in the process of prediction and forecasting. The discovery of previously unknown motifs therefore has considerable value. Studying the evolution of naive immune cells in their path to becoming memory provides a valuable insight into the way the immune system learns and adapts to recognise and remember the information it encounters. Memory cells represent the solutions that the immune system has generated and wishes to remember. This biological system represents a mechanism capable of finding and storing a solution to a problem. By understanding the evolutionary process that leads to

Abstract—Writer identification is one of the areas in pattern recognition that attract many researchers to work in, particularly in forensic and biometric application, where the writing style can be used as biometric features for authenticating an identity. The challenging task in writer identification is the extraction of unique features, in which the individualistic of such handwriting styles can be adopted into bio-inspired generalized global shape for writer identification. In this paper, the feasibility of generalized global shape concept of complimentary binding in Artificial Immune System (AIS) for writer identification is explored. An experiment based on the proposed framework has been conducted to proof the validity and feasibility of the proposed approach for off-line writer identification. Keywords—Writer identification, generalized global shape, individualistic, pattern recognition. I I.

The discovery of new functionalities through the study of human physiology has contributed toward the evolution of Artificial Immune Systems. In this chapter we can investigate a new architecture through observations of natural immunological behaviour, for which application to known algorithms contributed toward an improved performance. It

Abstract — Artificial Immune Systems (AIS) are algorithms inspired by the human immune system. The human immune system is a robust, decentralized, error tolerant and adaptive system. Such properties are highly desirable for the development of novel computer systems. Unlike some other bio-inspired techniques, such as genetic algorithms and neural networks, the field of AIS encompasses a spectrum of algorithms to implement different functions. In this paper we investigate CLONALG for network intrusion classification. The Clonal Selection Algorithm (CLONALG) is inspired by the clonal selection theory of acquired immunity, which has shown success on broad range of engineering problem domains.