Experimenting with innate immunity (original) (raw)

libtissue - a Software System for Incorporating Innate Immunity into Artificial Immune Systems

2000

In a previous paper the authors argue the case for incorporating concepts from innate immunity into Artificial Immune Systems and present an outline for a conceptual frame- work for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were highlighted, and how such properties might be instantiated in artificial systems was discussed

libtissue - implementing innate immunity

2006 IEEE International Conference on Evolutionary Computation, 2006

In a previous paper the authors argued the case for incorporating ideas from innate immunity into artificial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were highlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step - the libtissue system.

Biological Inspiration for Artificial Immune Systems

Lecture Notes in Computer Science, 2007

Abstract. Artificial immune systems (AISs) to date have generally been inspired by naive biological metaphors. This has limited the effectiveness of these systems. In this position paper two ways in which AISs could be made more biologically realistic are discussed. We propose that AISs should draw their inspiration from organisms which possess only innate immune systems, and that AISs should employ systemic models of the immune system to structure their overall design. An outline of plant and invertebrate immune systems is presented, and a number of contemporary systemic models are reviewed. The implications for interdisciplinary research that more biologically-realistic AISs could have is also discussed.

Innate and Adaptive Principles for an Artificial Immune System

Lecture Notes in Computer Science, 2006

This paper summarises the current literature on immune system function and behaviour, including pattern recognition receptors, danger theory, central and peripheral tolerance, and memory cells. An artificial immune system framework is then presented based on the analogies of these natural system components and a rule and feature-based problem representation. A data set for intrusion detection is used to highlight the principles of the framework.

Advances in artificial immune systems

IEEE Computational Intelligence Magazine, 2006

D uring the last decade, the field of Artificial Immune System (AIS) is progressing slowly and steadily as a branch of Computational Intelligence (CI) as shown in .There has been increasing interest in the development of computational models inspired by several immunological principles. In particular, some are building models mimicking the mechanisms in the biological immune system (BIS) to better understand its natural processes and simulate its dynamical behavior in the presence of antigens/pathogens. Most of the AIS models, however, emphasize designing artifacts-computational algorithms, techniques using simplified models of various immunological processes and functionalities. Like other biologically-inspired techniques, such as artificial neural networks, genetic algorithms, and cellular automata, AISs also try to extract ideas from the BIS in order to develop computational tools for solving science and engineering problems. Although still relatively young, the Artificial Immune System (AIS) is emerging as an active and attractive field involving models, techniques and applications of greater diversity.

Immunity-based systems: A survey

Systems, Man, and Cybernetics, …, 1997

Biological systems such as human beings can be regarded as sophisticated information processing systems, and can be expected to provide inspiration for various ideas to science and engineering. Biologicallymotivated information processing systems can be classi ed into: brain-nervous systems (neural networks), genetic systems (evolutionary algorithms), and immune systems (arti cial immune systems). Among these, nervous systems and genetic systems have been widely applied to various elds. There have been a relative few applications of the immune system. This paper presents a survey of arti cial immune systems and provides numerous insights of immunity-based systems applications in science and engineering.

An Introduction to Artificial Immune Systems

Handbook of Natural Computing, 2012

The field of artificial immune systems (AIS) can be thought of comprising two threads of research: the employment of mathematical and computational techniques in the modelling of immunology, and the incorporation of immune system inspired mechanisms and metaphors in the development of engineering solutions. The former permits the integration of immunological data and sub-models into a coherent whole, which may be of value to immunologists in the facilitation of immunological understanding, hypothesis testing, and the direction of future research. The latter attempts harness the perceived properties of the immune system in the solving of engineering problems. This chapter concentrates on the latter: the development and application of immune inspiration to engineering solutions.

Architecture for an Artificial Immune System

Evolutionary Computation, 2000

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. many components interact locally to provide global protection, so there is no central control and hence no single point of failure. The IS is dynamic in that individual components are continually created, destroyed, and circulated throughout the body, which increases the temporal and spatial diversity of the IS. Finally, the IS is robust to errors (error tolerant) because the effect of any single IS action is small, so a few mistakes in classification and response are not catastrophic.

An interdisciplinary perspective on artificial immune systems

Evolutionary Intelligence, 2008

... Artificial Immune Systems (AIS) is a diverse area of research that attempts to bridge the divide between immunology and engineering and ... such as mathematical and computational modelling of immunology, abstraction from those models into algorithm (and system) design and ...