Theoretically optimal distributed anomaly detection

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

doi:10.1109/ICDMW.2009.40

Theoretically optimal distributed anomaly detection

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

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

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 language	English (US)
Title of host publication	ICDM Workshops 2009 - IEEE International Conference on Data Mining
Pages	515-520
Number of pages	6
DOIs	https://doi.org/10.1109/ICDMW.2009.40
State	Published - 2009
Event	2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009 - Miami, FL, United States Duration: Dec 6 2009 → Dec 6 2009

Publication series

Name	ICDM Workshops 2009 - IEEE International Conference on Data Mining

Other

Other	2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009
Country/Territory	United States
City	Miami, FL
Period	12/6/09 → 12/6/09

Keywords

Anomaly detection
Data mining
Distributed

Publisher link

10.1109/ICDMW.2009.40

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Theoretically optimal distributed anomaly detection. / Lazarevic, Aleksandar; Srivastava, Nisheeth; Tiwari, Ashutosh et al.
ICDM Workshops 2009 - IEEE International Conference on Data Mining. 2009. p. 515-520 5360461 (ICDM Workshops 2009 - IEEE International Conference on Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lazarevic, A, Srivastava, N, Tiwari, A, Isom, J, Oza, NC & Srivastava, J 2009, Theoretically optimal distributed anomaly detection. in ICDM Workshops 2009 - IEEE International Conference on Data Mining., 5360461, ICDM Workshops 2009 - IEEE International Conference on Data Mining, pp. 515-520, 2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009, Miami, FL, United States, 12/6/09. https://doi.org/10.1109/ICDMW.2009.40

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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.",

keywords = "Anomaly detection, Data mining, Distributed",

author = "Aleksandar Lazarevic and Nisheeth Srivastava and Ashutosh Tiwari and Josh Isom and Oza, {Nikunj C.} and Jaideep Srivastava",

year = "2009",

doi = "10.1109/ICDMW.2009.40",

language = "English (US)",

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series = "ICDM Workshops 2009 - IEEE International Conference on Data Mining",

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AU - Lazarevic, Aleksandar

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AU - Tiwari, Ashutosh

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AU - Oza, Nikunj C.

AU - Srivastava, Jaideep

PY - 2009

Y1 - 2009

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AB - 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.

KW - Anomaly detection

KW - Data mining

KW - Distributed

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U2 - 10.1109/ICDMW.2009.40

DO - 10.1109/ICDMW.2009.40

M3 - Conference contribution

AN - SCOPUS:77951166524

SN - 9780769539027

T3 - ICDM Workshops 2009 - IEEE International Conference on Data Mining

SP - 515

EP - 520

BT - ICDM Workshops 2009 - IEEE International Conference on Data Mining

T2 - 2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009

Y2 - 6 December 2009 through 6 December 2009

ER -

Theoretically optimal distributed anomaly detection

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