Detecting fraudulent activity in a cloud using privacy-friendly data aggregates
Discusses a method to detect fraudulent activity (launching DDoS attacks, Bitcoin mining, …) by using non-intrusive, privacy-friendly data: billing data.
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- 2026-10-10
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Reproducible Machine Learning for Credit Card Fraud detection fraud-detection-handbook.github.io
A practical handbook on how to identify patterns in transactions.
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Five Fun Fraud Facts web.archive.org
Another tiny collection of features we can feed to a ML system to detect fraud. Comments on HN also unearthed more qualifying signals and derived geodata on the transaction.
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The Challenges of Operating a Computing Cloud and Charging for its Use web.stanford.edu
Skip the first 90% of this presentation by an AWS VP (which is about general system reliability). The last four slides are a good summary of what billing cloud services entails, especially the use of soft quotas to limit fraud.
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Maximizing Profit of Cloud Brokers under Quantized Billing Cycles: a Dynamic Pricing Strategy based on Ski-Rental… arxiv.org
“The key idea of our algorithms is to use pricing signal to regulate user demand. One may argue that such an algorithm gives poor service to the user as it pushes tasks out of the queue in order to maximize cloud broker's profit.”
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Privacy Oracle: a System for Finding Application Leakswith Black Box Differential Testing dl.acm.org
(CCS) Privacy Oracle: a system that uncovers applications' leaks of personal information in transmissions to remoteservers.
Billing › Fraud: “Discusses a method to detect fraudulent activity (launching DDoS attacks, Bitcoin mining, …) by using non-intrusive, privacy-friendly data: billing data.”