Hierarchical Anomaly Detection in Distributed Large-Scale Sensor Networks

Chatzigiannakis, Vassilis; Papavassiliou, Symeon; Grammatikou, Mary; Maglaris, Vasilis

doi:10.1109/iscc.2006.1691116

Cited by 50 publications

(35 citation statements)

References 7 publications

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“…They are of particular interest since analysts would like to have a better understanding of overall data characteristics in WSNs. Depending on the network architecture, the identification of global outliers can be performed in many different nodes (Chatzigiannakis et al, 2006). In a centralized architecture, all data is transmitted to the sink node for identifying outliers.…”

Section: Type Of Outliersmentioning

confidence: 99%

“…Specifically, the top few principal components capture the build of variability and any data instance that violates this structure for the smallest components is considered as an outlier (Chandola et al, 2007). Chatzigiannakis et al (2006) propose a PCA-based technique to solve data integrity and accuracy problem caused by compromised or malfunctioning sensor nodes. This technique uses PCA to efficiently model the spatio-temporal data correlations in a distributed manner and identifies local outliers spanning through neighboring nodes.…”

Section: Spectral Decomposition-based Approachesmentioning

confidence: 99%

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Outlier Detection Techniques for Wireless Sensor Networks: A Survey

Zhang

Meratnia

Havinga

2010

IEEE Commun. Surv. Tutorials

641

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In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the multivariate nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a decision tree to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier degree.

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Section: Type Of Outliersmentioning

confidence: 99%

Section: Spectral Decomposition-based Approachesmentioning

confidence: 99%

Outlier Detection Techniques for Wireless Sensor Networks: A Survey

Zhang

Meratnia

Havinga

2010

IEEE Commun. Surv. Tutorials

641

View full text Add to dashboard Cite

show abstract

“…More complex situations arise in multisensor systems where it is important to discriminate between corrupted data, faulty sensor nodes, and interesting events such as intrusion [18], [14], [11], [48], [62]. The various scenarios cannot be distinguished by simple point anomaly detection, but more sophisticated reasoning is required [39].…”

Section: Related Workmentioning

confidence: 99%

Domain Anomaly Detection in Machine Perception: A System Architecture and Taxonomy

Kittler

Christmas

Campos

et al. 2014

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-We address the problem of anomaly detection in machine perception. The concept of domain anomaly is introduced as distinct from the conventional notion of anomaly used in the literature. We propose a unified framework for anomaly detection which exposes the multifacetted nature of anomalies and suggest effective mechanisms for identifying and distinguishing each facet as instruments for domain anomaly detection. The framework draws on the Bayesian probabilistic reasoning apparatus which clearly defines concepts such as outlier, noise, distribution drift, novelty detection (object, object primitive), rare events, and unexpected events. Based on these concepts we provide a taxonomy of domain anomaly events. One of the mechanisms helping to pinpoint the nature of anomaly is based on detecting incongruence between contextual and noncontextual sensor(y) data interpretation. The proposed methodology has wide applicability. It underpins in a unified way the anomaly detection applications found in the literature. To illustrate some of its distinguishing features, in here the domain anomaly detection methodology is applied to the problem of anomaly detection for a video annotation system.

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“…A drawback of these techniques is that they are computationally intensive to be implementable in real-time on sensor nodes. At last, spectral decomposition based approaches [12] use principal component analysis (PCA) based techniques to identify normal modes of behaviour in data sets, at the expense of a high computational complexity.…”

Section: Outliers Detection and Accommodationmentioning

confidence: 99%

A Multi-Agent System for Outliers Accommodation in Wireless Sensor Networks

Gil¹,

Palma²,

Santos

et al. 2014

IJCEE

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Abstract:In monitoring applications the accuracy of data is paramount. When considering wireless sensor networks the quality of readings taken from the environment may be hampered by outliers in raw data collected from transmitters attached to nodes' analogue-to-digital converter ports. To improve the data quality sent to the base-station, a real-time data analysis should be implemented at nodes' level, while taking into account their computing power and storage limitations. This paper deals with the problem of outliers detection and accommodation in raw data. The proposed approach relies on univariate statistics within an hierarchical multi-agent framework. Results from experiments on a real monitoring scenario, at a major oil refinery plant, show the relevance of the proposed approach.

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Hierarchical Anomaly Detection in Distributed Large-Scale Sensor Networks

Cited by 50 publications

References 7 publications

Outlier Detection Techniques for Wireless Sensor Networks: A Survey

Outlier Detection Techniques for Wireless Sensor Networks: A Survey

Domain Anomaly Detection in Machine Perception: A System Architecture and Taxonomy

A Multi-Agent System for Outliers Accommodation in Wireless Sensor Networks

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