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 Scopus citations


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 (US)
Title of host publicationICDM Workshops 2009 - IEEE International Conference on Data Mining
Number of pages6
StatePublished - Dec 1 2009
Event2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009 - Miami, FL, United States
Duration: Dec 6 2009Dec 6 2009


Other2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009
CountryUnited States
CityMiami, FL


  • Anomaly detection
  • Data mining
  • Distributed

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