Anomaly detection in sensor systems using lightweight machine learning

H.H.W.J. Bosman, A. Liotta, G. Iacca, H.J. Wörtche

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

31 Citaten (Scopus)
3 Downloads (Pure)

Samenvatting

The maturing field of Wireless Sensor Networks (WSN) results in long-lived deployments that produce large amounts of sensor data. Lightweight online on-mote processing may improve the usage of their limited resources, such as energy, by transmitting only unexpected sensor data (anomalies). We detect anomalies by analyzing sensor reading predictions from a linear model. We use Recursive Least Squares (RLS) to estimate the model parameters, because for large datasets the standard Linear Least Squares Estimation (LLSE) is not resource friendly. We evaluate the use of fixed-point RLS with adaptive thresholding, and its application to anomaly detection in embedded systems. We present an extensive experimental campaign on generated and real-world datasets, with floating-point RLS, LLSE, and a rule-based method as benchmarks. The methods are evaluated on prediction accuracy of the models, and on detection of anomalies, which are injected in the generated dataset. The experimental results show that the proposed algorithm is comparable, in terms of prediction accuracy and detection performance, to the other LS methods. However, fixed-point RLS is efficiently implementable in embedded devices. The presented method enables online on-mote anomaly detection with results comparable to offline LS methods.
Originele taal-2Engels
Titel2013 IEEE International Conference on Systems, Man and Cybernetics (SMC), 13-16 October 2013, Manchester
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's7-13
ISBN van geprinte versie978-1-4799-0652-9
DOI's
StatusGepubliceerd - 2013
Evenement2013 IEEE Internationak Conference on Systems, Man, and Cybernetics (SMC 2013) - Manchester, Verenigd Koninkrijk
Duur: 13 okt. 201316 okt. 2013

Congres

Congres2013 IEEE Internationak Conference on Systems, Man, and Cybernetics (SMC 2013)
Verkorte titelSMC 2013
Land/RegioVerenigd Koninkrijk
StadManchester
Periode13/10/1316/10/13
AnderSystems, Man, and Cybernetics (SMC)

Vingerafdruk

Duik in de onderzoeksthema's van 'Anomaly detection in sensor systems using lightweight machine learning'. Samen vormen ze een unieke vingerafdruk.

Citeer dit