  The detection of anomalies in unknown environments is a problem that has been
approached from different perspectives with variable results. Artificial Immune
Systems (AIS) present particularly advantageous characteristics for the
detection of such anomalies. This research is based on an existing detector
model, named Artificial Bioindicators System (ABS) which identifies and solves
its main weaknesses. An ABS based anomaly classifier model is presented,
incorporating elements of the AIS. In this way, a new model (R-ABS) is
developed which includes the advantageous capabilities of an ABS plus the
reactive capabilities of an AIS to overcome its weaknesses and disadvantages.
The RABS model was tested using the well-known DARPA'98 dataset, plus a dataset
built to carry out a greater number of experiments. The performance of the RABS
model was compared to the performance of the ABS model based on classical
sensitivity and specificity metrics, plus a response time metric to illustrate
the rapid response of R-ABS relative to ABS. The results showed a better
performance of R-ABS, especially in terms of detection time.
