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In the human body, the adaptive immune system fights germs by remembering previous infections so it can respond quickly if the same pathogens return. This complex process depends on the cooperation of many cell types. Among these are T helpers, which assist by coordinating the response of other parts of the immune system -- called effector cells -- such as T killer and B cells. When an invading pathogen is detected, antigen presenting cells bring an identifying piece of the germ to a T cell. Certain T cells become activated and multiply many times in a process known as clonal selection.

Artificial Immune Systems have been successfully applied to a number of problem domains including fault tolerance and data mining, but have been shown to scale poorly when applied to computer intrusion detec- tion despite the fact that the biological immune system is a very effective anomaly detector. This may be because AIS algorithms have previously been based on the adaptive immune system and biologically-naive mod- els. This paper focuses on describing and testing a more complex and biologically-authentic AIS model, inspired by the interactions between the innate and adaptive immune systems. Its performance on a realistic process anomaly detection problem is shown to be better than standard AIS methods (negative-selection), policy-based anomaly detection methods (systrace), and an alternative innate AIS approach (the DCA). In addition, it is shown that runtime information can be used in combination with system call information to enhance detection capability.

Innate immunity now occupies a central role in immunology. However, artificial immune system models have largely been inspired by adaptive not innate immunity. This paper reviews the biological principles and properties of innate immunity and, adopting a conceptual framework, asks how these can be incorporated into artificial models. The aim is to outline a meta-framework for models of innate immunity.

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.