Abstract
The Artificial Immune Systems (AIS) constitute an emerging and very promising area of research that historically have been falling within two main theoretical immunological schools of thought: those based on Negative selection (NS) or those inspired on Danger theory (DT). Despite their inherent strengths and well known promising results, both deployed AIS have documented difficulties on dealing with gradual dynamic changes of self behavior through time.In this paper we propose and describe the development of an AIS framework for anomaly detection based on a rather different immunological theory, which is the Grossman’s Tunable Activation Thresholds (TAT) theory for the behaviour of T-cells. The overall framework has been tested with artificially generated stochastic data sets based on a real world phenomena and the results thus obtained have been compared with a non-evolutionary Support Vector Machine (SVM) classifier, thus demonstrating TAT’s performance and competitiveness for anomaly detection.
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