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2009 IEEE International Conference on Data Mining Workshops (2009)
Miami, Florida, USA
Dec. 6, 2009 to Dec. 6, 2009
ISBN: 978-0-7695-3902-7
pp: 515-520
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.

N. Srivastava, J. Srivastava, A. Tiwari, J. Isom, A. Lazarevic and N. Oza, "Theoretically Optimal Distributed Anomaly Detection," 2009 IEEE International Conference on Data Mining Workshops(ICDMW), Miami, Florida, USA, 2009, pp. 515-520.
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