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https://arxiv.org/abs/2203.02124
4 Mar 2022
https://netflixtechblog.com/machine-learning-for-fraud-detection-in-streaming-services-b0b4ef3be3f6

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.

Hello Prescience !

I'm going to start cramming the papers which are interesting me over a few months.
In that regard, I created my very own dark-knowledge: https://github.com/clouedoc/reading-list

I'd like to know what tools you recommend for learning this kind of content (i.e. RemNote, Anki...)

This could make a nice section of the README.md, to help people like me 😁

I may be thanking you too much, but again, thank you for your open-source projects 😊!

I thought this might be useful:
https://www.usenix.org/system/files/sec21-laperdrix.pdf

Year: 2021
Title: Fingerprinting in Style: Detecting Browser Extensions via Injected Style Sheets
Topics: Extensions
Authors: Pierre Laperdrix, Oleksii Starov, Quan Chen, Alexandros Kapravelos, Nick Nikiforakis