LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Support Vector Data Description
Machine Learning
IEEE Transactions on Computers
Spatio-temporal correlation: theory and applications for wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: In memroy of Olga Casals
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Estimating the Support of a High-Dimensional Distribution
Neural Computation
An online support vector machine for abnormal events detection
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
RETRACTED: Impacts of sensor node distributions on coverage in sensor networks
Journal of Parallel and Distributed Computing
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Data collected by Wireless Sensor Networks (WSNs) are inherently unreliable. Therefore, to ensure high data quality, secure monitoring, and reliable detection of interesting and critical events, outlier detection mechanisms are needed to be in place. The constraint nature of resources available in WSNs necessities that unlike traditional outlier detection techniques performed off-line, outliers to be identified in an online manner. This means that outliers in distributed streaming data should be detected in (near) real time with a high accuracy while maintaining the resource consumption of the WSN to a minimum. In this paper, we propose outlier detection techniques based on one-class quarter-sphere support vector machine meeting constraints and requirements of WSNs. To reduce the false alarm rate while increasing the detection rate and to enable collaborative outliers detection, we take advantage of spatial and temporal correlations that exist between sensor data. Experiments with both synthetic and real data show that our distributed and online outlier detection techniques achieve better detection accuracy and lower false alarm compared to an earlier distributed, batch outlier detection technique designed for WSNs.