Finding ghosts in your data : anomaly detection techniques with examples in Python /

Discover key information buried in the noise of data by learning a variety of anomaly detection techniques and using the Python programming language to build a robust service for anomaly detection against a variety of data types. The book starts with an overview of what anomalies and outliers are an...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Feasel, Kevin (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York : Apress, 2022.
Temas:
Computer programming.
Anomaly detection (Computer security)
Python (Computer program language)
Programmation (Informatique)
Détection d'anomalies (Sécurité informatique)
Python (Langage de programmation)
computer programming.
Computer programming
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Part I. What Is an Anomaly?
  • The Importance of Anomalies and Anomaly Detection
  • Humans are pattern matchers
  • Formalizing anomaly detection
  • Part II. Building an anomaly detector
  • Laying out the framework
  • Building a test suite
  • Implementing the first methods
  • Extending the ensemble
  • Visualize the results
  • Part III. Multivariate anomaly detection
  • Clustering and anomalies
  • Connectivity-based outlier factor (COF)
  • Local correlation integral (LOCI)
  • Copula-based outlier detection (COPOD)
  • Part IV. Time series anomaly detection
  • Time and anomalies
  • Change point detection
  • An introduction to multi-series anomaly detection
  • Standard deviation of difference (DIFFSTD)
  • Symbolic aggregate approximation (SAX)
  • Part V. Stacking up to the competition
  • Configuring azure cognitive services anomaly detector
  • Performing a bake-off
  • Bibliography.

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