Theoretically Optimal Distributed Anomaly Detection
Shared by Nikunj Oza, updated on Feb 26, 2012
Summary
- Author(s) :
- Aleksander Lazarevic, Nisheeth Srivastava, Ashutosh Tewari, Josh Isom, Nikunj Oza, Jaideep Srivastava
- 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 Ôzero information lossÕ. We further report experimental results on synthetic as well as real-world data to demonstrate the viability of our approach.
- Publication Name
- Proceedings of the IEEE International Conference on Data Mining (ICDM), Workshop on Mining on Mining Multiple Information Sources
- Publication Location
- Miami Beach, FL, USA
- Year Published
- 2009
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