Excerpt

Immune systems are remarkable examples of complex adaptive systems. They are decentralized, massively distributed, and able to adaptively respond to novel circumstances. While a better understanding of immune systems has many potential benefits for human health, such understanding could also help with the creation of artificial systems with similar capabilities. “Self–Nonself Discrimination in a Computer” was the first attempt to take specific models of the human immune system and apply them to a computer system. This paper presents a technique for detecting modified files based on a theoretical model of immune-system pattern recognition. The presented negative selection algorithm inspired significant follow-on work, as did the general idea of using the immune system as a template for novel computer defenses. It is foundational both because of the work it inspired and because it shows how much work remains at the intersection of computer science and immunology.

This paper is part of a long-term collaboration between the co- authors Stephanie Forrest (a computer scientist) and Alan Perelson (an immunologist) that continues to this day. The bulk of this work has focused on modeling the human immune system, particularly on the functioning of immune-system memory. Unlike many other approaches that use differential equations to model immune-system dynamics, their work explores the dynamics of bitstrings (strings of 1s and 0s) and bitstring matching. Bitstrings are used to represent antigen/antibody interactions, with closer matches between them corresponding to stronger recognition of potential infectious agents. Bitstring matching had been previously used by Pentti Kanerva to model the associative memory of the human brain; the immune system can also be seen as a kind of associative memory, and so its dynamics can also be modeled using similar techniques (Smith, Forrest, and Perelson 1999).

Bibliography

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