Abstract
This paper presents an artificial immune system (AIS) based on Grossman’s tunable activation threshold (TAT) for temporal anomaly detection. We describe the generic AIS framework and the TAT model adopted for simulating T Cells behaviour, emphasizing two novel important features: the temporal dynamic adjustment of T Cells clonal size and its associated homeostasis mechanism. We also present some promising results obtained with artificially generated data sets, aiming to test the appropriateness of using TAT in dynamic changing environments, to distinguish new unseen patterns as part of what should be detected as normal or as anomalous. We conclude by discussing results obtained thus far with artificially generated data sets.
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Acknowledgements
The authors acknowledge the facilities provided by the CRACS research unit, an INESC associate of the Faculty of Science, University of Porto.
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Antunes, M.J., Correia, M.E. (2010). Temporal Anomaly Detection: An Artificial Immune Approach Based on T Cell Activation, Clonal Size Regulation and Homeostasis. In: Arabnia, H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5913-3_33
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DOI: https://doi.org/10.1007/978-1-4419-5913-3_33
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