A survey on outlier detection in Internet of Things big data

A survey on outlier detection in Internet of Things big data

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In this chapter, more than one criteria are combined for better review and characterization of OD techniques. First, we classified the solutions based on big data phase criteria into data-generation and data-acquisition phases. Then, we categorized the techniques that detect outlier in data-acquisition phase on outlier type into fault detection, event detection, and intrusion detection. We classified the fault-detection techniques based on OD method into statistical based, machine learning, distance based, and density based. Thereafter, based on if the techniques assume the underlying distribution model and estimate the parameters of the model or not, are classified into parametric based and nonparametric based, respectively. We classified the machine learning techniques depending on if the user influences machine-learning technique or not, into supervised and unsupervised techniques, which are classified further based on the analyzing approach. Moreover, we categorized the distance-based and density-based techniques based on distance and density measurements.

Chapter Contents:

  • 11.1 Introduction
  • 11.2 Outliers-detection techniques
  • 11.3 Requirements and performance metrics
  • 11.4 Statistical-based techniques
  • 11.4.1 Parametric based
  • Gaussian model based
  • Regression model based
  • 11.4.2 Nonparametric based
  • Histograms
  • Kernel functions
  • 11.5 Machine learning
  • 11.5.1 Unsupervised learning
  • Partitioning-clustering methods
  • Hierarchical-clustering methods
  • Grid-based clustering methods
  • Density-based clustering methods
  • 11.5.2 Supervised learning
  • Support vector machines (SVMs) methods
  • Isolation-forest methods
  • Mahalanobis-distance methods
  • 11.6 Distance-based techniques
  • 11.6.1 Local neighborhood
  • 11.6.2 k-Nearest neighbors
  • 11.7 Density-based techniques
  • 11.7.1 Local outlier factor
  • 11.7.2 Connectivity-based outlier factor
  • 11.7.3 INFLuenced outlierness
  • 11.7.4 Multi-granularity deviation factor
  • 11.8 Conclusion
  • References

Inspec keywords: Big Data; parameter estimation; unsupervised learning; Internet of Things; pattern classification; data acquisition; statistical analysis

Other keywords: supervised technique; big data phase criteria; fault detection; data-generation phase; event detection; OD techniques; machine learning; distance measurement; Internet of Things big data; density measurement; density-based technique; unsupervised technique; intrusion detection; data-acquisition phase; outlier detection; distance-based technique

Subjects: Other topics in statistics; Ubiquitous and pervasive computing; Data handling techniques; Knowledge engineering techniques

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