  Accumulation of corporate data in the cloud has attracted more enterprise
applications to the cloud creating data gravity. As a consequence, network
traffic has become more cloud centric. This increase in cloud centric traffic
poses new challenges in designing learning systems for streaming data due to
class imbalance. The number of classes plays a vital role in the accuracy of
the classifiers built from the data streams. In this paper, we present a vector
quantization-based sampling method, which substantially reduces the class
imbalance in data streams. We demonstrate its effectiveness by conducting
experiments on network traffic and anomaly dataset with commonly used ML model
building methods; Multilayered Perceptron on TensorFlow backend, Support Vector
Machines, K-Nearest Neighbour, and Random Forests. We built models using
parallel processing, batch processing, and randomly selecting samples. We show
that the accuracy of classification models improves when the data streams are
pre-processed with our method. We used out of the box hyper-parameters of these
classifiers and auto sklearn for hyperparameter optimization.
