Compressive anomaly detection in large networks

Xiao Li, H. Vincent Poor, Anna Scaglione

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages985-988
Number of pages4
DOIs
StatePublished - Dec 1 2013
Externally publishedYes
Event2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
Duration: Dec 3 2013Dec 5 2013

Publication series

Name2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Other

Other2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
CountryUnited States
CityAustin, TX
Period12/3/1312/5/13

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

Fingerprint Dive into the research topics of 'Compressive anomaly detection in large networks'. Together they form a unique fingerprint.

  • Cite this

    Li, X., Poor, H. V., & Scaglione, A. (2013). Compressive anomaly detection in large networks. In 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings (pp. 985-988). [6737058] (2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings). https://doi.org/10.1109/GlobalSIP.2013.6737058