The use of novelty detection techniques for monitoring high-integrity plant

King, Stephen P.; King, Dennis; Astley, K.; Tarassenko, Lionel; Hayton, Paul; Utete, Simukai W.

doi:10.1109/cca.2002.1040189

Cited by 25 publications

(12 citation statements)

References 5 publications

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“…For instance, Theiler and Cai (2003) describe how level set estimation and anomaly detection go along in the context of multispectral image analysis, where anomalous locations (pixels) correspond to unusual spectral signatures in these images. Further areas of anomaly detection include intrusion detection [e.g., Fan et al (2001) and Yeung and Chow (2002)], anomalous jet engine vibrations [e.g., Nairac et al (1997), Desforges, Jacob and Cooper (1998) and King et al (2002)] or medical imaging [e.g., Gerig, Jomier and Chakos (2001) and Prastawa et al (2003)] and EEG-based seizure analysis [Gardner et al (2006)]. For a recent review of this area see Markou and Singh (2003).…”

mentioning

confidence: 99%

Asymptotic normality of plug-in level set estimates

Mason¹,

Polonik²

2009

Ann. Appl. Probab.

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We establish the asymptotic normality of the $G$-measure of the symmetric difference between the level set and a plug-in-type estimator of it formed by replacing the density in the definition of the level set by a kernel density estimator. Our proof will highlight the efficacy of Poissonization methods in the treatment of large sample theory problems of this kind.Comment: Published in at http://dx.doi.org/10.1214/08-AAP569 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

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confidence: 99%

Asymptotic normality of plug-in level set estimates

Mason¹,

Polonik²

2009

Ann. Appl. Probab.

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“…If it does not, it is classified as anomalous. This technique is widely used in audio signal anomaly detection, novelty detection, and system call intrusion detection (S. King et al, 2002).…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Application of Anomaly Detection Techniques to Identify Fraudulent Refunds

Issa

Vasarhelyi

2011

SSRN Journal

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“…If a test instance lies outside the hypersphere, it is declared to be anomalous. Many SVMbased techniques have been proposed for anomaly detection in musical signal data [7], fault diagnosis in machinery [8], novelty detection in power generation plants [9], seizure analysis from intracranial electroencephalogram [10], and intrusion detection [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection combining one-class SVMs and particle swarm optimization algorithms

Jiang

2010

Nonlinear Dyn

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Anomalies are patterns in data that do not conform to a well-defined notion of normal behavior. One-class Support Vector Machines calculate a hyperplane in the feature space to distinguish anomalies, but the false positive rate is always high and parameter selection is a key issue. So, we propose a novel one-class framework for detecting anomalies, which takes the advantages of both boundary movement strategy and the effectiveness of evaluation algorithm on parameters optimization. First, we search the parameters by using a particle swarm optimization algorithm. Each particle suggests a group of parameters, the area under receiver operating characteristic curve is chosen as the fitness of the object function. Second, we improve the original decision function with a boundary movement. After the threshold has been adjusted, the final detection function will bring about a high detection rate with a lower false positive rate. Experimental results on UCI data sets show that the proposed method can achieve better performance than other one class learning schemes.

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The use of novelty detection techniques for monitoring high-integrity plant

Cited by 25 publications

References 5 publications

Asymptotic normality of plug-in level set estimates

Asymptotic normality of plug-in level set estimates

Application of Anomaly Detection Techniques to Identify Fraudulent Refunds

Anomaly detection combining one-class SVMs and particle swarm optimization algorithms

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