We will be demonstrating the results of our research into the implementation of a host-based Autonomic Defense System (ADS) using a Partially-Observable Markov Decision Process. The goal of an ADS is to "relexively" respond to an attack, thwarting it to the extent that humans have time to form a tactical response to the attack. A defensive system that automatically responds to an attack must meet two criteria: it must select the correct response in the face of an attack, and it must not take actions to attacks that are not there. This challenge is exaccerbated by the fact that, in order to detect never-before-seen attacks, the ADS must use anomally detectors for its sensor input - anomaly detectors typically have relatively high false posiive and falst negative rates. Thus, key to an ADS is a controller that can obtain a valid signal from a noisy sensor. The ALPHATECH Lightweight Autonomic Defense System (αLADS) is a prototype ADS constructed around a PO-MDP stochastic controller. The state model allows the controller to filter out the false positives from the anomaly sensor such that authorized processes are not killed and false alerts are not issued. We will demonstrate αLADS defending against Internet worms operating in real time.