Dynamic combination of multiple host-based anomaly detectors with broader detection coverage and fewer false alerts

To achieve broader detection coverage with fewer false alarms, a POMDP-based anomaly detection model combining several sate-of-the-art host-based anomaly detectors is proposed in this paper. An optimal combinatorial manner is expected to be discovered through a policy-gradient reinforcement learning algorithm, based on the independent actions of those detectors, and the behavior of the proposed model can be adjusted through a global reward signal to adapt to various system situations. A primarily experiment with some comparative studies are carried out to validate its performance.