Unsupervised Outlier Detection in Sensor Networks Using Aggregation Tree

Zhang, Kejia; Shi, Shengfei; Gao, Hong; Li, Jianzhong

doi:10.1007/978-3-540-73871-8_16

Cited by 59 publications

(30 citation statements)

References 10 publications

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“…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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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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“…In a similar setting, Zhang et al [89] describe a technique for identification of global outliers, where outliers are defined as the n points with the largest distance to their k th nearest neighbor. This technique assumes the existence of an aggregation tree, which is used as the communication structure among the nodes in the network.…”

Section: Node Similarity-basedmentioning

confidence: 99%

Real-Time Data Analytics in Sensor Networks

Palpanas

2012

Managing and Mining Sensor Data

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Abstract. The proliferation of Wireless Sensor Networks (WSNS) in the past decade has provided the bridge between the physical and digital worlds, enabling the monitoring and study of physical phenomena at a granularity and level of detail that was never before possible. In this study, we review the efforts of the research community with respect to two important problems in the context of WSNS: real-time collection of the sensed data, and real-time processing of these data series.

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“…Since outliers are assumed to occur far less frequently than normal objects, it does not make economic sense to undertake a laborious process of developing clusters for normal objects only to find a few outliers in the end. Recent unsupervised outlier detection methods [22] [15] [44] incorporate ideas to handle outliers without explicitly and completely finding clusters of normal objects.…”

Section: The Assumption Based Approachmentioning

confidence: 99%

Multi-level relationship outlier detection

Jiang¹,

Campbell²,

Tang³

et al. 2012

IJBIDM

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Relationship management is critical in business. Particularly, it is important to detect abnormal relationships, such as fraudulent relationships between service providers and consumers. Surprisingly, in the literature there is no systematic study on detecting relationship outliers. Particularly, no existing methods can detect and handle relationship outliers between groups and individuals in groups. In this thesis, we tackle this important problem by developing a simple yet effective model. We identify two types of outliers and devise efficient detection algorithms. Our experiments on both real data sets and synthetic ones confirm the effectiveness and efficiency of our approach.

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Unsupervised Outlier Detection in Sensor Networks Using Aggregation Tree

Cited by 59 publications

References 10 publications

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

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

Real-Time Data Analytics in Sensor Networks

Multi-level relationship outlier detection

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