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

TL;DR

This paper introduces R-ABS, an enhanced anomaly detection model combining bioindicators and immune system principles, demonstrating improved detection speed and accuracy on benchmark datasets.

Contribution

It presents a novel R-ABS model that integrates AIS reactive capabilities into an existing bioindicator-based detector, improving anomaly detection performance.

Findings

R-ABS outperforms ABS in detection speed

R-ABS shows higher sensitivity and specificity

R-ABS responds more rapidly to anomalies

Abstract

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.