TY - GEN
T1 - Compressive anomaly detection in large networks
AU - Li, Xiao
AU - Poor, H. Vincent
AU - Scaglione, Anna
PY - 2013
Y1 - 2013
N2 - This paper considers a large sensor network with its nodes taking measurements from certain distributions, while a small subset of the nodes draw anomalous measurements from distributions that differ from the majority. Since all the distributions are unknown a priori, the compressive anomaly detection (CAD) algorithm is proposed at the fusion center to identify the set of anomalous sensors and estimate both the common and anomaly distributions, using only few compressed sensor observations under the type-based multiple access (TB-MA) protocol. Simulations demonstrate that the proposed CAD algorithm can efficiently single out the set of anomalies and estimate the distributions accurately.
AB - This paper considers a large sensor network with its nodes taking measurements from certain distributions, while a small subset of the nodes draw anomalous measurements from distributions that differ from the majority. Since all the distributions are unknown a priori, the compressive anomaly detection (CAD) algorithm is proposed at the fusion center to identify the set of anomalous sensors and estimate both the common and anomaly distributions, using only few compressed sensor observations under the type-based multiple access (TB-MA) protocol. Simulations demonstrate that the proposed CAD algorithm can efficiently single out the set of anomalies and estimate the distributions accurately.
UR - http://www.scopus.com/inward/record.url?scp=84897723238&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897723238&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2013.6737058
DO - 10.1109/GlobalSIP.2013.6737058
M3 - Conference contribution
AN - SCOPUS:84897723238
SN - 9781479902484
T3 - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
SP - 985
EP - 988
BT - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
T2 - 2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
Y2 - 3 December 2013 through 5 December 2013
ER -