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The detection of anomalies in unknown environments is a problem that has beenapproached from different perspectives with variable results. Artificial ImmuneSystems (AIS) present particularly advantageous characteristics for thedetection of such anomalies. This research is based on an existing detectormodel, named Artificial Bioindicators System (ABS) which identifies and solvesits main weaknesses. An ABS based anomaly classifier model is presented,incorporating elements of the AIS. In this way, a new model (R-ABS) isdeveloped which includes the advantageous capabilities of an ABS plus thereactive capabilities of an AIS to overcome its weaknesses and disadvantages.The RABS model was tested using the well-known DARPA'98 dataset, plus a datasetbuilt to carry out a greater number of experiments. The performance of the RABSmodel was compared to the performance of the ABS model based on classicalsensitivity and specificity metrics, plus a response time metric to illustratethe rapid response of R-ABS relative to ABS. The results showed a betterperformance of R-ABS, especially in terms of detection time.

A secondary immune response based on co-evolutive populations of agents for anomaly detection and characterization