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Anomaly detection has a long history, particularly in statistics, where it is

known as outlier detection. Relevant books on the topic are those of Barnett

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and Lewis [464], Hawkins [483], and Rousseeuw and Leroy [513]. The article

by Beckman and Cook [466] provides a general overview of how statisticians

look at the subject of outlier detection and provides a history of the subject

dating back to comments by Bernoulli rn 1777. Also see the related articles

1467, 4841. Another general article on outlier detection is the one by Barnett

[+0f1. erti”les on finding outliers in multivariate data include those by Davies

and Gather 1474), Gnanadesikan and Kettenring 1480], Rocke and woodruff

[tr11], Rousr””n* and van Zomerenand 1515], and Scott [516]. Rosner [512] provides a discussion of finding multiple outliers at the same time.

An extensive survey of outlier detection methods is provided by Hodge and

Austin [486]. Markou and singh [506, 507] give a two-part review of techniques

for novelty detection that covers statistical and neural network techniques,

respectively. Grubbs’ procedure for detecting outliers was originally described

in la81]. Thg mixture model outlier approach discussed in section 10-2’3 is

from Eskin [476]. The notion of a distance-based outlier and the fact that this

definition can include many statistical definitions of an outlier was described

by Knorr et al. 1496-498]. The LoF technique (Breunig et al. [468, 469])

grew out of DBSCAN. Ramaswamy et al. 1510] propose a distance-based

outlier detection procedure that gives each object an outlier score based on

the distance of its k-nearest neighbor. Efficiency is achieved by partitioning

the data using the first phase of BIRCH (Section 9.5.2). Chaudhary et al.

[470] use k-d trees to improve the efficiency of outlier detection, while Bay and

schwabacher [465] use randomization and pruning to improve performance.

Aggarwal and Yu [462] use projection to address outlier detection for high-

 

 

676 Chapter 1O Anomaly Detection

dimensional data, while Shyu et al. [518] use an approach based on principal components. A theoretical discussion of outlier removal in high-dimensional space can be found in the paper by Dunagan and Vempala [475]. The use of information measures in anomaly detection is described by Lee and Xiang

1504], while an approach based on the 12 measure is given by Ye and Chen

[520]. Many different types of classification techniques can be used for anomaly

detection. A discussion of approaches in the area of neural networks can be found in papers by Hawkins et al. [485], Ghosh and Schwartzbard 1479], and Sykacek [519]. Recent work on rare class detection includes the work of Joshi et al. [490-494]. The rare class problem is also sometimes referred to as the imbalanced data set problem. Of relevance are an AAAI workshop (Japkowicz

1488]), an ICML workshop (Chawla et al. [a71]), and a special issue of SIGKDD Explorations (Chawla et al. la72l).

Clustering and anomaly detection have a long relationship. In Chapters 8 and 9, we considered techniques, such as BIRCH, CURE, DENCLUE, DB- SCAN, and SNN density-based clustering, which specifically include tech- niques for handling anomalies. Statistical approaches that discuss this re- lationship are described in papers by Scott [516] and Hardin and Rocke [482].

In this chapter, we have focused on basic anomaly detection schemes. We have not considered schemes that take into account the spatial or temporal nature of the data. Shekhar et al. [517] provide a detailed discussion of the problem of spatial outliers and present a unified approach to spatial outlier detection. The issue of outliers in time series was first considered in a sta- tistically rigorous way by Fox [478]. Muirhead [508] provides a discussion of different types of outliers in time series. Abraham and Chuang [ 6t] propose a Bayesian approach to outliers in time series, while Chen and Liu [473] consider different types of outliers in time series and propose a technique to detect them and obtain good estimates of time series parameters. Work on finding deviant or surprising patterns in time series databases has been performed by Jagadish et aI. [487] and Keogh et al. [495]. Outlier detection based on geometric ideas, such as the depth of convex hulls, has been explored in papers by Johnson et al. [489], Liu et al. [505], and Rousseeuw et al. [51a].

An important application area for anomaly detection is intrusion detection. Surveys of the applications of data mining to intrusion detection are given by Lee and Stolfo [502] and Lazarevic et al. [501]. In a different paper, Lazarevic et al. [500] provide a comparison of anomaly detection routines specific to network intrusion. A framework for using data mining techniques for intrusion detection is provided by Lee et al. [503]. Clustering-based approaches in the

 

 

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