Survey of Anomaly Detection Methods

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This survey defines the problem of anomaly detection and provides an overview of existing methods. The methods are categorized into two general classes: generative and discriminative. A generative approach involves building a model that represents the joint distribution of the input features and the output labels of system behavior (e.g., normal or anomalous) then applies the model to formulate a decision rule for detecting anomalies. On the other hand, a discriminative approach aims directly to find the decision rule, with the smallest error rate, that distinguishes between normal and anomalous behavior. For each approach, we will give an overview of ... continued below

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PDF-file: 40 pages; size: 5.3 Mbytes

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Ng, B October 12, 2006.

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Description

This survey defines the problem of anomaly detection and provides an overview of existing methods. The methods are categorized into two general classes: generative and discriminative. A generative approach involves building a model that represents the joint distribution of the input features and the output labels of system behavior (e.g., normal or anomalous) then applies the model to formulate a decision rule for detecting anomalies. On the other hand, a discriminative approach aims directly to find the decision rule, with the smallest error rate, that distinguishes between normal and anomalous behavior. For each approach, we will give an overview of popular techniques and provide references to state-of-the-art applications.

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PDF-file: 40 pages; size: 5.3 Mbytes

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  • Report No.: UCRL-TR-225264
  • Grant Number: W-7405-ENG-48
  • DOI: 10.2172/900157 | External Link
  • Office of Scientific & Technical Information Report Number: 900157
  • Archival Resource Key: ark:/67531/metadc887987

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Office of Scientific & Technical Information Technical Reports

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Creation Date

  • October 12, 2006

Added to The UNT Digital Library

  • Sept. 22, 2016, 2:13 a.m.

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  • Dec. 7, 2016, 10:47 p.m.

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Ng, B. Survey of Anomaly Detection Methods, report, October 12, 2006; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc887987/: accessed June 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.