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Anomaly Detection Principles and Algorithms

Part of the Terrorism, Security, and Computation Book Series

DOI: 10.1007/978-3-319-67526-8

ISBN: 978-3-319-67524-4

Publisher: Springer International Publishing - Springer Journals

Oct 31, 2017

1 - 9 of 9 Chapters

LitStream

Introduction

Mehrotra, Kishan G.; Mohan, Chilukuri K.; Huang, HuaMing

Pages: 3 –19

[Incidents of fraud have increased at a rapid pace in recent years, perhaps because very simple technology (such as email) is sufficient to help miscreants commit fraud. Losses may not be directly financial, e.g., an email purportedly from a family member may pretend to communicate a photograph,...
LitStream

Anomaly Detection

Mehrotra, Kishan G.; Mohan, Chilukuri K.; Huang, HuaMing

Pages: 21 –32

[Anomaly detection problems arise in multiple applications, as discussed in the preceding chapter. such as financial fraud, cyber intrusion, video surveillance, and medical image analysis. This chapter discusses the basic ideas of anomaly detection, and sets up a framework within which various...
LitStream

Distance-Based Anomaly Detection Approaches

Mehrotra, Kishan G.; Mohan, Chilukuri K.; Huang, HuaMing

Pages: 33 –39

[In this chapter we consider anomaly detection based on distance (similarity) measures. Our approach is to explore various possible scenarios in which an anomaly may arise. To keep things simple, in most of the chapter we illustrate basic concepts using one-dimensional observations. Distance...
LitStream

Clustering-Based Anomaly Detection Approaches

Mehrotra, Kishan G.; Mohan, Chilukuri K.; Huang, HuaMing

Pages: 41 –55

[This chapter explores anomaly detection approaches based on explicit identification of clusters in a data set. Points that are not within a cluster become candidates to be considered anomalies. Variations among algorithms result in evaluating the relative anomalousness of points that are near...
LitStream

Model-Based Anomaly Detection Approaches

Mehrotra, Kishan G.; Mohan, Chilukuri K.; Huang, HuaMing

Pages: 57 –94

[Many data sets are described by models that may capture the underlying processes that lead to generation of data, describing a presumed functional or relational relationship between relevant variables. Such models permit comprehension and concise description of the data sets, facilitating...
LitStream

Distance and Density Based Approaches

Mehrotra, Kishan G.; Mohan, Chilukuri K.; Huang, HuaMing

Pages: 97 –117

[In Chap. 3, we discussed distance based approaches for anomaly detection; however there the focus was to illustrate how distances can be measured and minor perturbation in proposed distance can change the outcome; illustrated by simple examples. In this chapter we consider anomaly detection...
LitStream

Rank Based Approaches

Mehrotra, Kishan G.; Mohan, Chilukuri K.; Huang, HuaMing

Pages: 119 –134

[Density-based methodology that exploits k-neighborhood of a data point has many good features. For instance, it is independent of the distribution of the data and is capable of detecting isolated objects. However it has some shortcomings:]
LitStream

Ensemble Methods

Mehrotra, Kishan G.; Mohan, Chilukuri K.; Huang, HuaMing

Pages: 135 –152

[In the previous chapters, we have described various anomaly detection algorithms, whose relative performance varies with the dataset and the application being considered.]
LitStream

Algorithms for Time Series Data

Mehrotra, Kishan G.; Mohan, Chilukuri K.; Huang, HuaMing

Pages: 153 –189

[Many practical problems involve data that arrive over time, and are hence in a strict temporal sequence. As discussed in Chap. 5, treating the data as a set, while ignoring the time-stamp, loses information essential to the problem. Treating the time-stamp as just another dimension (on par with...

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