Wavelet Methods for the Detection of Anomalies and their Application to Network Traffic Analysis

Kwon, Deukwoo; Ko, Kyungduk; Vannucci, Marina; Reddy, A. L. Narasimha; Kim, S.

doi:10.1002/qre.781

Cited by 26 publications

(14 citation statements)

References 20 publications

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Section: Resultsmentioning

confidence: 99%

“…CPA has been used to explore changes in mean, variance and regression coefficient in data from a range of disciplines such as bioinformatics (Lio & Vannucci 2000), network and traffic analyses (Kwon et al 2006), climatology (Reeves et al 2007), econometrics (Perron & Yabu 2009) and engineering (Killick et al 2010). CPA has been used to explore changes in mean, variance and regression coefficient in data from a range of disciplines such as bioinformatics (Lio & Vannucci 2000), network and traffic analyses (Kwon et al 2006), climatology (Reeves et al 2007), econometrics (Perron & Yabu 2009) and engineering (Killick et al 2010).…”

Section: Identifying Change Points and Clustersmentioning

confidence: 99%

See 1 more Smart Citation

How salient is thenurse~squaremerger?

Watson¹,

Clark²

2013

English Language and Linguistics

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This article reports the results of an experimental investigation into listeners' evaluative reactions towards the NURSE∼SQUARE merger in the north-west of England, in an attempt to shed light on its salience. Although speakers across England's north-west have a NURSE∼SQUARE merger, its realisation differs: in Liverpool, speakers typically merge to a mid front [E…], while speakers from St Helens, just 20km further east, merge to a mid central [‰…]. To test listeners' responses to each variant, we presented two groups of listeners from each of these localities with read sentence data from a single speaker. The speaker was from the north-west of England and had a centralised NURSE∼SQUARE vowel in his native accent (representing the St Helens model). To achieve a matched-guise, the original NURSE∼SQUARE vowels were acoustically manipulated to give the impression of fronting (representing the Liverpool model). Listeners from Liverpool and St Helens were asked to react to guises along the status dimension, and their reaction was measured in real-time using bespoke audience response software administered via the web. The novelty in this approach is that it can be used not only to show that listeners do indeed react to the guises, but also to examine precisely when this reaction takes place. Our results show that (a) overall, speakers with a NURSE∼SQUARE merger are not rated highly on the status dimension, regardless of whether they have a merger to a front or central vowel; (b) listeners' real-time reactions can be correlated with instances of NURSE and SQUARE; and (c) listeners' responses to NURSE can be different from responses to SQUARE. We discuss these results in relation to the salience of this merger in particular and to salience in general. We suggest that the salience of NURSE and SQUARE is related to the local social context and the micro-linguistic context in which they appear.

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Section: Resultsmentioning

confidence: 99%

Section: Identifying Change Points and Clustersmentioning

confidence: 99%

How salient is thenurse~squaremerger?

Watson¹,

Clark²

2013

English Language and Linguistics

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“…Increasingly the ability to detect change points quickly and accurately is of interest to a wide range of disciplines. Recent examples of application areas include numerous bioinformatic applications [37,15] the detection of malware within software [51], network traffic analysis [35], finance [46], climatology [32] and oceanography [34].…”

Section: Introductionmentioning

confidence: 99%

Analysis of changepoint models

Eckley¹,

Fearnhead²,

Killick³

2011

Bayesian Time Series Models

102

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“…They applied their estimators once a control chart signals indicating the presence of an assignable cause of variability. Kwon et al 18 developed an integrated tool for the online detection of network anomalies considering statistical change-point detection algorithms for both local changes in the variance and for the detection of jumps. They also proposed modified versions of these algorithms based on moving window techniques.…”

Section: Introductionmentioning

confidence: 99%

A clustering approach to identify the time of a step change in Shewhart control charts

Ghazanfari

Alaeddini

Niaki

et al. 2008

Quality & Reliability Eng

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Control charts are the most popular statistical process control tools used to monitor process changes. When a control chart indicates an out-of-control signal it means that the process has changed. However, control chart signals do not indicate the real time of process changes, which is essential for identifying and removing assignable causes and ultimately improving the process. Identifying the real time of the change is known as the change-point estimation problem. Most of the traditional methods of estimating the process change point are developed based on the assumption that the process follows a normal distribution with known parameters, which is seldom true. In this paper, we propose clustering techniques to estimate Shewhart control chart change points. The proposed approach does not depend on the true values of the parameters and even the distribution of the process variables. Accordingly, it is applicable to both phase-I and phase-II of normal and non-normal processes. At the end, we discuss the performance of the proposed method in comparison with the traditional procedures through extensive simulation studies.

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Wavelet Methods for the Detection of Anomalies and their Application to Network Traffic Analysis

Cited by 26 publications

References 20 publications

How salient is thenurse~squaremerger?

How salient is thenurse~squaremerger?

Analysis of changepoint models

A clustering approach to identify the time of a step change in Shewhart control charts

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