Nikunj C. Oza's Publications

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Theoretically Optimal Distributed Anomaly Detection

Theoretically Optimal Distributed Anomaly Detection. Aleksander Lazarevic, Nisheeth Srivastava, Ashutosh Tewari, Josh Isom, Nikunj Oza, and Jaideep Srivastava. In Proceedings of the IEEE International Conference on Data Mining (ICDM), Workshop on Mining on Mining Multiple Information Sources., 2009.

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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.

BibTeX Entry

@inproceedings{lasr09,
	author = {Aleksander Lazarevic, Nisheeth Srivastava, Ashutosh Tewari, Josh Isom, Nikunj Oza, and Jaideep Srivastava},
	title = {Theoretically Optimal Distributed Anomaly Detection},
	booktitle={Proceedings of the IEEE International Conference on Data Mining (ICDM), Workshop on Mining on Mining Multiple Information Sources.},
	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.},
	bib2html_pubtype = {Refereed Conference},
	bib2html_rescat = {Anomaly Detection},
	year = {2009}
}

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