Metabolic Systemic Computing: Exploiting Innate Immunity within an Artificial Organism for On-line Self-Organisation and Anomaly Detection
- 64 Downloads
Previous work suggests that innate immunity and representations of tissue can be useful when combined with artificial immune systems. Here we provide a new implementation of tissue for artificial immune systems using systemic computation, a new model of computation and corresponding computer architecture based on a systemics world-view and supplemented by the incorporation of natural characteristics. We show using systemic computation how to create an artificial organism, a program with metabolism that eats data, expels waste, self-organise cells based on the nature of its food and emits danger signals suitable for an artificial immune system. The implementation is tested by application to two standard machine learning sets and shows excellent abilities to recognise anomalies in its diet as well as a consistent datawise self-organisation.
KeywordsSystemic computation Tissue Innate immunity Anomaly detection Self-organisation Danger theory Artificial organism Artificial metabolism Artificial immune system
Unable to display preview. Download preview PDF.
- 1.Aickelin, U., Greensmith, J.: Sensing danger: innate immunology for intrusion detection. Elsevier information security technical report, pp. 218–227. Elsevier, Amsterdam (2007)Google Scholar
- 2.Matzinger, P.: Tolerance, danger and the extended family. Annu. Rev. Immunol. 12, 991–1045 (1994)Google Scholar
- 3.Bentley, P.J., Greensmith, J., Ujjin, S.: Two ways to grow tissue for artificial immune systems. In: Proc. of the Fourth Intl. Conf. on Artificial Immune Systems (ICARIS 2005). LNCS 3627, pp. 139–152. Springer, New York (2005)Google Scholar
- 5.Breast Cancer Wisconsin (Diagnostic) Dataset: Creator: Wolberg, W.H., Donor: Mangasarian, O. UCI machine learning repository. http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic) (1992)
- 6.Wine Dataset: original owners: Forina, M., et al., PARVUS, Donor: Aeberhard, S. UCI machine learning repository. http://archive.ics.uci.edu/ml/datasets/Wine (1991)
- 7.Tempesti, G., Roggen, D., Sanchez, E., Thoma, Y.: A POEtic architecture for bio-inspired hardware. In: Proc. of the 8th Intl. Conf. on the Simulation and Synthesis of Living Systems (Artificial Life VIII), pp. 111–115. MIT, Cambridge (2002)Google Scholar
- 10.von Neumann, J.: The Theory of Self-reproducing Automata. University of Illinois Press, Champaign (1966)Google Scholar
- 16.Le Martelot, E., Bentley, P.J., Lotto, R.B.: Exploiting natural asynchrony and local knowledge within systemic computation to enable generic neural structures. In: Proc. of 2nd International Workshop on Natural Computing (IWNC 2007), Nagoya, 10–12 December 2007Google Scholar
- 17.Le Martelot, E., Bentley, P.J., Lotto, R.B.: Crash-Proof systemic computing: a demonstration of native fault-tolerance and self-maintenance. In: Proc. of 4th IASTED International Conference on Advances in Computer Science and Technology (ACST 2008). ACTA, Anaheim (2008)Google Scholar
- 18.Greensmith, J., Aickelin, U., Cayzer, S.: Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection. In: Proc. of the Fourth Intl. Conf. on Artificial Immune Systems (ICARIS 2005). LNCS 3627, pp. 153–167. Springer, New York (2005)Google Scholar