Theoretically optimal distributed anomaly detection

Aleksandar Lazarevic, Nisheeth Srivastava, Ashutosh Tiwari, Josh Isom, Nikunj C. Oza, Jaideep Srivastava

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

4 Citations (Scopus)

Abstract

A novel general framework for distributed anomaly detection with theoretical performance guarantees is proposed. Our algorithmic approach combines existing anomaly detection procedures with a novel method for computing global statistics using local sufficient statistics. Under a Gaussian assumption, our distributed algorithm is guaranteed to perform as well as its centralized counterpart, a condition we call 'zero information loss'. We further report experimental results on synthetic as well as real-world data to demonstrate the viability of our approach.

Original languageEnglish
Title of host publicationICDM Workshops 2009 - IEEE International Conference on Data Mining
Pages515-520
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2009
Event2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009 - Miami, FL, United States
Duration: 6 Dec 20096 Dec 2009

Publication series

NameICDM Workshops 2009 - IEEE International Conference on Data Mining

Conference

Conference2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009
CountryUnited States
CityMiami, FL
Period6/12/096/12/09

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Keywords

  • Anomaly detection
  • Data mining
  • Distributed

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Lazarevic, A., Srivastava, N., Tiwari, A., Isom, J., Oza, N. C., & Srivastava, J. (2009). Theoretically optimal distributed anomaly detection. In ICDM Workshops 2009 - IEEE International Conference on Data Mining (pp. 515-520). [5360461] (ICDM Workshops 2009 - IEEE International Conference on Data Mining). https://doi.org/10.1109/ICDMW.2009.40