1 research outputs found
Anomaly Detection and Sampling Cost Control via Hierarchical GANs
Anomaly detection incurs certain sampling and sensing costs and therefore it
is of great importance to strike a balance between the detection accuracy and
these costs. In this work, we study anomaly detection by considering the
detection of threshold crossings in a stochastic time series without the
knowledge of its statistics. To reduce the sampling cost in this detection
process, we propose the use of hierarchical generative adversarial networks
(GANs) to perform nonuniform sampling. In order to improve the detection
accuracy and reduce the delay in detection, we introduce a buffer zone in the
operation of the proposed GAN-based detector. In the experiments, we analyze
the performance of the proposed hierarchical GAN detector considering the
metrics of detection delay, miss rates, average cost of error, and sampling
ratio. We identify the tradeoffs in the performance as the buffer zone sizes
and the number of GAN levels in the hierarchy vary. We also compare the
performance with that of a sampling policy that approximately minimizes the sum
of average costs of sampling and error given the parameters of the stochastic
process. We demonstrate that the proposed GAN-based detector can have
significant performance improvements in terms of detection delay and average
cost of error with a larger buffer zone but at the cost of increased sampling
rates.Comment: 6 pages, 7 figures, has been accepted by Globecom 202