Pattern-based Method for Anomaly Detection in Sensor Networks

Ines Ben Kraiem, Faiza Ghozzi, Andre Peninou, Olivier Teste

Abstract

The detection of anomalies in real fluid distribution applications is a difficult task, especially, when we seek to accurately detect different types of anomalies and possible sensor failures. Resolving this problem is increasingly important in building management and supervision applications for analysis and supervision. In this paper we introduce CoRP ”Composition of Remarkable Points” a configurable approach based on pattern modelling, for the simultaneous detection of multiple anomalies. CoRP evaluates a set of patterns that are defined by users, in order to tag the remarkable points using labels, then detects among them the anomalies by composition of labels. By comparing with literature algorithms, our approach appears more robust and accurate to detect all types of anomalies observed in real deployments. Our experiments are based on real world data and data from the literature.

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Paper Citation


in Harvard Style

Ben Kraiem I., Ghozzi F., Peninou A. and Teste O. (2019). Pattern-based Method for Anomaly Detection in Sensor Networks.In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-372-8, pages 104-113. DOI: 10.5220/0007736701040113


in Bibtex Style

@conference{iceis19,
author={Ines Ben Kraiem and Faiza Ghozzi and Andre Peninou and Olivier Teste},
title={Pattern-based Method for Anomaly Detection in Sensor Networks},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2019},
pages={104-113},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007736701040113},
isbn={978-989-758-372-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Pattern-based Method for Anomaly Detection in Sensor Networks
SN - 978-989-758-372-8
AU - Ben Kraiem I.
AU - Ghozzi F.
AU - Peninou A.
AU - Teste O.
PY - 2019
SP - 104
EP - 113
DO - 10.5220/0007736701040113