Article

Network flow for outlier detection

Authors:

Ying Liu,

Alan P. Sprague,

Elliot LefkowitzAuthors Info & Claims

ACMSE '04: Proceedings of the 42nd annual ACM Southeast Conference

Pages 402 - 103

https://doi.org/10.1145/986537.986634

Published: 02 April 2004 Publication History

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Abstract

Detecting outliers is an important topic in data mining. Sometimes the outliers are more interesting than the rest of the data. Outlier identification has lots of applications, such as intrusion detection, and unusual usage of credit cards or telecommunication services. In this paper, we propose a novel method for outlier identification which is based on network flow. We use the well known Maximum Flow Minimum Cut theorem from graph theory to find the outliers and strong outlier groups. Especially, it works on high dimensional data. This outlier detection occurs in a novel setting: to repair poor quality clusters generated by a clustering algorithm.

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Cited By

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Jaswal KKumar PRawat S(2015)Design and development of a prototype application for intrusion detection using data mining2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)10.1109/ICRITO.2015.7359266(1-6)Online publication date: Sep-2015
https://doi.org/10.1109/ICRITO.2015.7359266
Ying Liu Sprague A(2004)Outlier detection and evaluation by network flow2004 International Conference on Machine Learning and Applications, 2004. Proceedings.10.1109/ICMLA.2004.1383547(436-442)Online publication date: 2004
https://doi.org/10.1109/ICMLA.2004.1383547

Index Terms

Network flow for outlier detection
1. Information systems
  1. Information retrieval
  2. Information storage systems

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Information & Contributors

Information

Published In

ACMSE '04: Proceedings of the 42nd annual ACM Southeast Conference

April 2004

485 pages

ISBN:1581138709

DOI:10.1145/986537

General Chair:
Seong-Moo Yoo
The University of Alabama in Huntsville, Huntsville, AL
,
Program Chair:
Letha Hughes Etzkorn
The University of Alabama in Huntsville, Huntsville, AL

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 April 2004

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ACM SE04

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ACM SE04: ACM Southeast Regional Conference 2004

April 2 - 3, 2004

Alabama, Huntsville

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Overall Acceptance Rate 502 of 1,023 submissions, 49%

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Jaswal KKumar PRawat S(2015)Design and development of a prototype application for intrusion detection using data mining2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)10.1109/ICRITO.2015.7359266(1-6)Online publication date: Sep-2015
https://doi.org/10.1109/ICRITO.2015.7359266
Ying Liu Sprague A(2004)Outlier detection and evaluation by network flow2004 International Conference on Machine Learning and Applications, 2004. Proceedings.10.1109/ICMLA.2004.1383547(436-442)Online publication date: 2004
https://doi.org/10.1109/ICMLA.2004.1383547

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Outlier detection and evaluation by network flow

Enhancing Outlier Detection by an Outlier Indicator

Neighborhood outlier detection