  Streaming anomaly detection refers to the problem of detecting anomalous data
samples in streams of data. This problem poses challenges that classical and
deep anomaly detection methods are not designed to cope with, such as
conceptual drift and continuous learning. State-of-the-art flow anomaly
detection methods rely on fixed memory using hash functions or nearest
neighbors that may not be able to constrain high-frequency values as in a
moving average or remove seamless outliers and cannot be trained in an
end-to-end deep learning architecture. We present a new incremental anomaly
detection method that performs continuous density estimation based on random
Fourier features and the mechanism of quantum measurements and density matrices
that can be viewed as an exponential moving average density. It can process
potentially endless data and its update complexity is constant $O(1)$. A
systematic evaluation against 12 state-of-the-art streaming anomaly detection
algorithms using 12 streaming datasets is presented.
