This paper investigates the role of negative selection in an artificial immune system (AIS) for network intrusion detection. The work focuses on the use of negative selection as a network traffic anomaly detector. The results of the negative selection algorithm experiments show a severe scaling problem for handling real network traffic data. The paper concludes by suggesting that the most appropriate use of negative selection in the AIS is as a filter for invalid detectors, not the generation of competent detectors. 1

The field of Artificial Immune Systems (AIS) concerns the study and development of computationally interesting abstractions of the immune system. This survey tracks the development of AIS since its inception, and then attempts to make an assessment of its usefulness, defined in terms of ‘distinctiveness ’ and ‘effectiveness. ’ In this paper, the standard types of AIS are examined—Negative Selection, Clonal Selection and Immune Networks—as well as a new breed of AIS, based on the immunological ‘danger theory. ’ The paper concludes that all types of AIS largely satisfy the criteria outlined for being useful, but only two types of AIS satisfy both criteria with any certainty.

This thesis explores the design and application of artificial immune systems (AISs), problem-solving systems inspired by the human and other immune systems. AISs to date have largely been modelled on the biological adaptive immune system and have taken little inspiration from the innate immune system. The first part of this thesis examines the biological innate immune system, which controls the adaptive immune system. The importance of the innate immune system suggests that AISs should also incorporate models of the innate immune system as well as the adaptive immune system. This thesis presents and discusses a number of design principles for AISs which are modelled on both innate and adaptive immunity. These novel design principles provided a structured framework for developing AISs which incorporate innate and adaptive immune systems in general. These design principles are used to build a software system which allows such AISs to be implemented and explored.

artificial immune system. This document reviews recent efforts in the area of Artificial immune systems (AIS) and their applications for (ad hoc) wireless networks. It presents basic mechanism of Human immune systems, introduces the reader to the learning paradigms of AIS, sums up misbehavior in ad hoc wireless networks and discusses pros and cons of AIS in increasing robustness of ad hoc wireless networks against misbehavior.

In this paper, we propose an algorithm to solve multiobjective optimization problems (either constrained or unconstrained) using the clonal selection principle. Our approach is compared with respect to another algorithm that is representative of the state-ofthe -art in evolutionary multiobjective optimization.

on Metacognition in Computation. Menlo Park, CA: AAAI

artificial immune system, literature survey. We review recent approaches to dealing with misbehavior in ad hoc wireless networks. We focus on a specific class of solutions that are based on autonomic computing. This class is motivated by the very efficient and complex system that is able to protect the health of humans against an amazing set of malicious extraneous attacks. We also provide the reader with a summary of misbehavior that is currently considered in the literature. Certain aspects of machine learning and game theory with relevance to misbehavior detection are reviewed as well. Based on relevant design properties of Artificial immune systems motivated by human immunity we present an outline of a four-layer architecture for ad hoc wireless networks. The purpose of this architecture is to impose a high degree of survivability against misbehavior of nodes.

In this paper we show how an Artificial Immune System can be used to study pattern recognition processes and learning. In particular we show the ability of the model to discover and maintain coverage of the diverse patterns through mechanism of evolution and mutation.

doi:10.1093/bioinformatics/bti426

In this paper, we propose an improved version of a constraint-handling scheme based on a model of the immune system. The approach is coupled to a genetic algorithm and used for global optimization. Our experiments indicate that the approach is relatively easy to implement, and that it is also efficient (computationally speaking).