  This work presents a fraud and abuse detection framework for streaming
services by modeling user streaming behavior. The goal is to discover anomalous
and suspicious incidents and scale the investigation efforts by creating models
that characterize the user behavior. We study the use of semi-supervised as
well as supervised approaches for anomaly detection. In the semi-supervised
approach, by leveraging only a set of authenticated anomaly-free data samples,
we show the use of one-class classification algorithms as well as autoencoder
deep neural networks for anomaly detection. In the supervised anomaly detection
task, we present a so-called heuristic-aware data labeling strategy for
creating labeled data samples. We carry out binary classification as well as
multi-class multi-label classification tasks for not only detecting the
anomalous samples but also identifying the underlying anomaly behavior(s)
associated with each one. Finally, using a systematic feature importance study
we provide insights into the underlying set of features that characterize
different streaming fraud categories. To the best of our knowledge, this is the
first paper to use machine learning methods for fraud and abuse detection in
real-world scale streaming services.
