Hierarchical anomaly detection in distributed large-scale sensor networks

Chatzigiannakis, V; Papavassiliou, S; Grammatikou, M; Maglaris, B

dc.contributor.author	Chatzigiannakis, V	en
dc.contributor.author	Papavassiliou, S	en
dc.contributor.author	Grammatikou, M	en
dc.contributor.author	Maglaris, B	en
dc.date.accessioned	2014-03-01T02:44:04Z
dc.date.available	2014-03-01T02:44:04Z
dc.date.issued	2006	en
dc.identifier.issn	15301346	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31649
dc.subject	Anomaly Detection	en
dc.subject	Data Collection	en
dc.subject	Data Gathering	en
dc.subject	Data Integrity	en
dc.subject	Large Scale	en
dc.subject	Sensor Network	en
dc.subject	Sensor Nodes	en
dc.subject	Use Case	en
dc.subject	Wireless Sensor Network	en
dc.subject.other	Anomaly detection	en
dc.subject.other	Malfunctioning nodes	en
dc.subject.other	Hierarchical systems	en
dc.subject.other	Meteorology	en
dc.subject.other	Real time systems	en
dc.subject.other	Sensor data fusion	en
dc.subject.other	Wireless sensor networks	en
dc.subject.other	Intrusion detection	en
dc.title	Hierarchical anomaly detection in distributed large-scale sensor networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ISCC.2002.1021759	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ISCC.2002.1021759	en
heal.identifier.secondary	1691116	en
heal.publicationDate	2006	en
heal.abstract	In this paper, an anomaly detection approach that fuses data gathered from different nodes in a distributed wireless sensor network is proposed and evaluated. The emphasis of this work is placed on the data integrity and accuracy problem caused by compromised or malfunctioning nodes. One of the key features of the proposed approach is that it provides an integrated methodology of taking into consideration and combining effectively correlated sensor data, in a distributed fashion, in order to reveal anomalies that span through a number of neighboring sensors. Furthermore, it allows the integration of results from neighboring network areas to detect correlated anomalies/attacks that involve multiple groups of nodes. The efficiency and effectiveness of the proposed approach is demonstrated for a real use case that utilizes meteorological data collected from a distributed set of sensor nodes. © 2006 IEEE.	en
heal.journalName	Proceedings - International Symposium on Computers and Communications	en
dc.identifier.doi	10.1109/ISCC.2002.1021759	en
dc.identifier.spage	761	en
dc.identifier.epage	766	en