Showing papers on "Artificial immune system published in 2000"

Architecture for an Artificial Immune System

Abstract: An artificial immune system (ARTIS) is described which incorporates many properties of natural immune systems, including diversity, distributed computation, error tolerance, dynamic learning and adaptation, and self-monitoring. ARTIS is a general framework for a distributed adaptive system and could, in principle, be applied to many domains. In this paper, ARTIS is applied to computer security in the form of a network intrusion detection system called LISYS. LISYS is described and shown to be effective at detecting intrusions, while maintaining low false positive rates. Finally, similarities and differences between ARTIS and Holland's classifier systems are discussed.

An artificial immune system for data analysis

Abstract: 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.

Artificial Immune Systems: A novel data analysis technique inspired by the immune network theory

Abstract: This thesis presents a novel data analysis technique inspired by the natural immune system. Immunological metaphors were extracted, simplified and applied to create an effective data analysis technique. This thesis builds on foundations of previous work, extracts salient features of the immune system and creates a principled and effective data analysis technique. Throughout this thesis, a methodical and principled approach was adopted. Previous work, along with background immunology was extensively surveyed. Problems with previous research were identified and principles from immunology were extracted to create the initial AIS for data analysis. The AIS, through the process of cloning and mutation, built up a network of B cells that were a diverse representation of data being analysed. This network was visualised via a specially developed tool. This allows the user to interact with the network and use the system for exploratory data analysis. Experiments were performed on two different data sets, a simple simulated data set and the Fisher Iris data set. Good results were obtained by the AIS on both sets, with the AIS being able to identify clusters known to exist within them. Extensive investigation into the algorithm's behaviour was undertaken and the way in which algorithm parameters effected performance and results was also examined. Despite initial success from the original AIS, problems were identified with the algorithm and the second stage of research was undertaken. This resulted in the resource limited artificial immune system (RLAIS) which created a stable network of objects that did not deteriorate or loose patterns once discovered. Periods of stable network size were observed with perturbations of the network size. This thesis presents a successful application of immune system metaphors to create a novel data analysis technique. Furthermore, the RLAIS goes a long way toward making AIS a viable contender for effective data analysis and further research is identified for study.

A formal model of an artificial immune system.

Abstract: The paper presents a mathematical model based on the features of antigen–antibody bindings in the immune system. In the natural immune system, local binding of immune cells and molecules to antigenic peptides is based generally on the behavior of surface proteins. In particular, immune cells contain proteins on their receptors, and apparently, these proteins play the key role both in immune response and recognition processes. In this work, we consider the immune cells in the form of formal B-cell and formal T-cell and develop a mathematical model of their interactions. We refer this model as the formal immune system (FIS). The paper provides an analysis of a network of bindings (or interactions) among the formal proteins of the FIS.

Generating Optimal Repertoire of Antibody Strings in an Artificial Immune System

Abstract: In this paper an idea of the artificial immune system (or AIS for brevity) is explained. Restricting to so-called binary AIS, methods for generating a repertoire of lymphocytes of minimal size are reviewed and new algorithm of low space complexity is discussed. Besides, recipes for counting so-called holes, as well as counting the total number of unrecognizable strings are given.

Artificial immune systems: application to autonomous agents

Abstract: The function of the immune system is to protect the living body against invaders through the use of defensive mechanisms. Some previous researchers have used artificial immune systems (AIS) to solve diverse engineering problems. The purpose of the paper is to apply the AIS technique to a distributed autonomous robotics system (DARS) problem. One of the classic problems in DARS is the dog and sheep problem. The authors try to benefit from the features of the natural immune system in the development of the dog and sheep problem. On the other hand, we find that natural immune systems are sophisticated information processors. They learn to recognize relevant patterns; they remember patterns that have been seen previously; and, they use diversity to promote robustness. Furthermore, the individual cells and molecules that comprise the immune system are distributed throughout the body, encoding and controlling the system in parallel, with no central control mechanism. The immune system uses several weapons to attack the foreign antigen. Abstractly, these weapons are the helper T-cells, B-cells, and antibodies. We simulated the dog as a B cell, the sheep as an antigen, the antibody as the dog behavior, the antigen response as the sheep behavior, and the sheep-to-pen distance as a helper T cell. The system interacts in an equivalent manner to the immune response, trying to restore the environment to its original state, which is the sheep inside the pen.

Investigating the evolution and stability of a resource limited artificial immune system.

Abstract: Timmis J and Neal M J. Investigating the evolution and stability of a resource limited artificial immune system. In Proceedings of GECCO - special workshop on artificial immune systems, pages 40-41. AAAI press, 2000.

Two models of immunization for time dependent optimization

Abstract: This paper investigates the relevance of the immune system metaphor to time-dependent optimization (TDO). First, we review previous results underlining the over-average reactiveness and robustness featured by Sais (Simple Artificial Immune System) in comparison to well-known evolutionist approaches. Then, we evaluate its immunization capability (i.e. improving its robustness to previously encountered optima) and eventually propose Yasais (Yet Another Simple Artificial Immune System).

Discriminative power of the receptors activated by k-contiguous bits rule

Abstract: The paper provides a brief introduction into a relatively new discipline: artificial immune systems (AIS). These are computer systems exploiting the natural immune system (or NIS for brevity) metaphor: protect an organism against invaders. Hence, a natural field of applications of AIS is computer security. But the notion of invader can be extended further: for instance a fault occurring in a system disturbs patterns of its regular functioning. Thus fault, or anomaly detection is another field of applications. It is convenient to represent the information about normal and abnormal functioning of a system in binary form (e.g. computer programs/viruses are binary files). Now the problem can be stated as follows: given a set of self patterns representing normal behaviour of a system under considerations find a set of detectors (i.e, antibodies, or more precisely, receptors) identifying all non self strings corresponding to abnormal states of the system. A new algorithm for generating antibody strings is presented. Its interesting property is that it allows to find in advance the number of of strings which cannot be detected by an "ideal" receptors repertoire.

Large-scale cellular automata simulations of the immune system response.

Abstract: The sequential nature of the process allowing the immune system to learn how to withstand pathogen agents is explored by means of large-scale computer simulation of the Celada-Seiden immunological automaton. In accord with our previous results, it is found that the learning process proceeds via a sequential cascade in affinity space.

On Parameter Adjustment of the Immune Inspired Machine Learning Algorithm AINE

Abstract: A machine-learning algorithm based on the natural immune system metaphor has been developed, AINE (Artificial Immune Network). AINE developed from initial work on Artificial Immune Systems for data analysis, for which detailed experimentation was undertaken as to the affect of altering algorithm parameters had on the behaviour of the system. Two of the parameters, the network affinity threshold and mutation rate have been carried over into the new version, AINE. A third parameter the number of resources has been introduced into AINE as a means by which to control network size and create a stable network structure. This paper provides details of experiments, which alter these three parameters in AINE. It was expected that the two parameters taken from the AIS would, when altered, exhibit the same behaviour in AINE, that being the NAT scalar affecting network connectivity and mutation rate affecting network size and connectivity. Indeed, this was found to be the case. The third parameter, was designed to create stable network and previous work has shown this to be the case. It is shown in this paper, that the esource parameter can be used to control population size within the network.

iNet: an extensible framework for simulating immune network

Abstract: The natural immune system is a subject of great research interest because it provides powerful and flexible information processing capabilities as a complex adaptive system. This paper describes our extensible framework, named iNet, for building artificial immune systems, particularly artificial immune networks, which has been used in several of our projects developing biologically-inspired intelligent applications. We describe the iNet architecture, its design principles and features, highlighting its versatility and component reusability. We also describe an autonomous decentralized network application built with iNet as a sample use case for showing how to use it.

Immune evolutionary algorithms

Abstract: Three evolutionary algorithms, the immune genetic algorithm (IGA), the immune evolutionary programming (IEP) and the immune evolutionary strategy (IES), are presented based on the immune theory in biology, which are not only convergent but used for solving complex discrete optimization problems as well. They all construct an immune operator accomplished by two components, vaccination and immune selection. The methods for selecting vaccines and constructing an immune operator are also proposed. Simulations show that these algorithms can restrain the degenerate phenomenon and improve the searching capability of the existing algorithms, therefore increase the convergent speed greatly.

The immune distributed competitive problem solver with major histocompatibility complex and immune network

Abstract: The objective is to propose an immune distributed competitive problem solver using MHC and an immune network and to verify its validity by means of computer simulations. Our algorithm solves the division-of-labor issues and problems for each agent work domain in a multi-agent system (MPS) by two immune functions. First, the Major Histocompatibility Complex (MHC) distinguishes "self" from "nonself", used in the process of eliminating states of competition. Second, the immune network that produces specific antibodies by modification of immune cells is used to produce adaptive behaviors for agents. Then, to investigate the validity of the proposed method, this algorithm is applied to the "N-th agent travelling salesmen problem (n-TSP)" as a typical case problem for multi-agent systems. The effectiveness of solving multi-agent system problems in this way is clarified through some simulations.

Reflexive Biodynamics for Immune Inspired Computing

Abstract: Artificial Immune Systems is a rapidly growing field of information processing based upon immune inspired paradigms of nonlinear dynamics.