Anomaly detection in wireless sensor networks in a non-stationary environment
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TLDR
This survey provides a comprehensive overview of approaches to anomaly detection in a WSN and their operation in a non-stationary environment.Abstract:
Anomaly detection in a WSN is an important aspect of data analysis in order to identify data items that significantly differ from normal data. A characteristic of the data generated by a WSN is that the data distribution may alter over the lifetime of the network due to the changing nature of the phenomenon being observed. Anomaly detection techniques must be able to adapt to a non-stationary data distribution in order to perform optimally. In this survey, we provide a comprehensive overview of approaches to anomaly detection in a WSN and their operation in a non-stationary environment.read more
Citations
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Machine learning algorithms for wireless sensor networks: A survey
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Anomaly Detection Methods for Categorical Data: A Review
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TL;DR: This article reviews 36 methods for the detection of anomalies in categorical data in both literatures and classify them into 12 different categories based on the conceptual definition of anomalies they use, and surveys anomaly detection methods.
References
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Reference EntryDOI
Principal Component Analysis
TL;DR: Principal component analysis (PCA) as discussed by the authors replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables.
Journal ArticleDOI
Anomaly detection: A survey
TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
Journal ArticleDOI
Wireless sensor network survey
TL;DR: This survey presents a comprehensive review of the recent literature since the publication of a survey on sensor networks, and gives an overview of several new applications and then reviews the literature on various aspects of WSNs.
Journal ArticleDOI
The use of the area under the ROC curve in the evaluation of machine learning algorithms
TL;DR: AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invariant to a priori class probabilities.