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Early Detection of Fraud Storms in the Cloud

  • Hani Neuvirth
  • Yehuda Finkelstein
  • Amit Hilbuch
  • Shai Nahum
  • Daniel Alon
  • Elad Yom-Tov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9286)

Abstract

Cloud computing resources are sometimes hijacked for fraudulent use. While some fraudulent use manifests as a small-scale resource consumption, a more serious type of fraud is that of fraud storms, which are events of large-scale fraudulent use. These events begin when fraudulent users discover new vulnerabilities in the sign up process, which they then exploit in mass. The ability to perform early detection of these storms is a critical component of any cloud-based public computing system.

In this work we analyze telemetry data from Microsoft Azure to detect fraud storms and raise early alerts on sudden increases in fraudulent use. The use of machine learning approaches to identify such anomalous events involves two inherent challenges: the scarcity of these events, and at the same time, the high frequency of anomalous events in cloud systems.

We compare the performance of a supervised approach to the one achieved by an unsupervised, multivariate anomaly detection framework. We further evaluate the system performance taking into account practical considerations of robustness in the presence of missing values, and minimization of the model’s data collection period.

This paper describes the system, as well as the underlying machine learning algorithms applied. A beta version of the system is deployed and used to continuously control fraud levels in Azure.

Keywords

Cloud Computing Anomaly Detection Cloud System Offer Type Supervise Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hani Neuvirth
    • 1
  • Yehuda Finkelstein
    • 1
  • Amit Hilbuch
    • 1
  • Shai Nahum
    • 1
  • Daniel Alon
    • 1
  • Elad Yom-Tov
    • 2
  1. 1.Azure Cyber-Security Group, MicrosoftHerzeliaIsrael
  2. 2.Microsoft ResearchHerzeliaIsrael

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