Automated Anomaly Detector Adaptation using Adaptive Threshold Tuning

Real-time network-and host-based Anomaly Detection Systems (ADSs) transform a continuous stream of input data into meaningful and quantifiable anomaly scores. These scores are subsequently compared to a fixed detection threshold and classified as either benign or malicious. We argue that a real-time ADS' input changes considerably over time and a fixed threshold value cannot guarantee good anomaly detection accuracy for such a time-varying input. In this article, we propose a simple and generic technique to adaptively tune the detection threshold of any ADS that works on threshold method. To this end, we first perform statistical and information-theoretic analysis of network-and host-based ADSs' anomaly scores to reveal a consistent time correlation structure during benign activity periods. We model the observed correlation structure using Markov chains, which are in turn used in a stochastic target tracking framework to adapt an ADS' detection threshold in accordance with real-time measurements. We also use statistical techniques to make the proposed algorithm resilient to sporadic changes and evasion attacks. In order to evaluate the proposed approach, we incorporate the proposed adaptive thresholding module into multiple ADSs and evaluate those ADSs over comprehensive and independently collected network and host attack datasets. We show that, while reducing the need of human threshold configuration, the proposed technique provides considerable and consistent accuracy improvements for all evaluated ADSs.