An Improved Composite Hypothesis Test for Markov Models with Applications in Network Anomaly Detection
For practitioners of network anomaly detection, this provides a more reliable test that better controls false alarms, especially with small sample sizes.
The paper improves the composite hypothesis test for Markov models by using a Central Limit Theorem (CLT) approximation to set thresholds more accurately, reducing false alarm rates in network anomaly detection compared to prior large deviations methods.
Recent work has proposed the use of a composite hypothesis Hoeffding test for statistical anomaly detection. Setting an appropriate threshold for the test given a desired false alarm probability involves approximating the false alarm probability. To that end, a large deviations asymptotic is typically used which, however, often results in an inaccurate setting of the threshold, especially for relatively small sample sizes. This, in turn, results in an anomaly detection test that does not control well for false alarms. In this paper, we develop a tighter approximation using the Central Limit Theorem (CLT) under Markovian assumptions. We apply our result to a network anomaly detection application and demonstrate its advantages over earlier work.