Diagnosing anomalies and identifying faulty nodes in sensor networks

Chatzigiannakis, V; Papavassiliou, S

dc.contributor.author	Chatzigiannakis, V	en
dc.contributor.author	Papavassiliou, S	en
dc.date.accessioned	2014-03-01T01:26:08Z
dc.date.available	2014-03-01T01:26:08Z
dc.date.issued	2007	en
dc.identifier.issn	1530-437X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17934
dc.subject	Anomaly detection	en
dc.subject	Principal component analysis (PCA)	en
dc.subject	Spatial correlation	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.classification	Instruments & Instrumentation	en
dc.subject.classification	Physics, Applied	en
dc.subject.other	Anomaly detection	en
dc.subject.other	Data integrity	en
dc.subject.other	Distributed sensor network	en
dc.subject.other	Spatial correlation	en
dc.subject.other	Computer crime	en
dc.subject.other	Data acquisition	en
dc.subject.other	Data fusion	en
dc.subject.other	Principal component analysis	en
dc.subject.other	Signal receivers	en
dc.subject.other	Sensor networks	en
dc.title	Diagnosing anomalies and identifying faulty nodes in sensor networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/JSEN.2007.894147	en
heal.identifier.secondary	http://dx.doi.org/10.1109/JSEN.2007.894147	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	In this paper, an anomaly detection approach that fuses data gathered from different nodes in a distributed 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. The proposed approach utilizes and applies Principal Component Analysis simultaneously on multiple metrics received from various sensors. 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. © 2007 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Sensors Journal	en
dc.identifier.doi	10.1109/JSEN.2007.894147	en
dc.identifier.isi	ISI:000246780600005	en
dc.identifier.volume	7	en
dc.identifier.issue	5	en
dc.identifier.spage	637	en
dc.identifier.epage	645	en