Detecting Anomalies in Graphs with Numeric Labels

Michael Davis; Weiru Liu; Paul Miller; George Redpath

doi:10.1145/2063576.2063749

Detecting Anomalies in Graphs with Numeric Labels

Michael Davis, Weiru Liu, Paul Miller, George Redpath

Research output: Contribution to conference › Paper › peer-review

24 Citations (Scopus)

Abstract

This paper presents Yagada, an algorithm to search labelled graphs for anomalies using both structural data and numeric attributes. Yagada is explained using several security-related examples and validated with experiments on a physical Access Control database. Quantitative analysis shows that in the upper range of anomaly thresholds, Yagada detects twice as many anomalies as the best-performing numeric discretization algorithm. Qualitative evaluation shows that the detected anomalies are meaningful, representing a com- bination of structural irregularities and numerical outliers.

Original language	English
Pages	1197-1202
Number of pages	6
DOIs	https://doi.org/10.1145/2063576.2063749
Publication status	Published - Oct 2011
Event	20th ACM Conference on Information and Knowledge Management - Glasgow, United Kingdom Duration: 24 Oct 2011 → 28 Oct 2011

Conference

Conference	20th ACM Conference on Information and Knowledge Management
Abbreviated title	CIKM 2011
Country	United Kingdom
City	Glasgow
Period	24/10/2011 → 28/10/2011

Bibliographical note

ISSN: 978-1-4503-0717-8

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10.1145/2063576.2063749

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R1118ECI: Centre for Secure Information Technologies (CSIT)
McCanny, J. V., Cowan, C., Crookes, D., Fusco, V., Linton, D., Liu, W., Miller, P., O'Neill, M., Scanlon, W. & Sezer, S.
01/08/2009 → 30/06/2014
Project: Research

Cite this

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title = "Detecting Anomalies in Graphs with Numeric Labels",

abstract = "This paper presents Yagada, an algorithm to search labelled graphs for anomalies using both structural data and numeric attributes. Yagada is explained using several security-related examples and validated with experiments on a physical Access Control database. Quantitative analysis shows that in the upper range of anomaly thresholds, Yagada detects twice as many anomalies as the best-performing numeric discretization algorithm. Qualitative evaluation shows that the detected anomalies are meaningful, representing a com- bination of structural irregularities and numerical outliers.",

author = "Michael Davis and Weiru Liu and Paul Miller and George Redpath",

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AU - Liu, Weiru

AU - Miller, Paul

AU - Redpath, George

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AB - This paper presents Yagada, an algorithm to search labelled graphs for anomalies using both structural data and numeric attributes. Yagada is explained using several security-related examples and validated with experiments on a physical Access Control database. Quantitative analysis shows that in the upper range of anomaly thresholds, Yagada detects twice as many anomalies as the best-performing numeric discretization algorithm. Qualitative evaluation shows that the detected anomalies are meaningful, representing a com- bination of structural irregularities and numerical outliers.

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DO - 10.1145/2063576.2063749

M3 - Paper

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EP - 1202

T2 - 20th ACM Conference on Information and Knowledge Management

Y2 - 24 October 2011 through 28 October 2011

ER -

Detecting Anomalies in Graphs with Numeric Labels

Abstract

Conference

Bibliographical note

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Projects

R1118ECI: Centre for Secure Information Technologies (CSIT)

Cite this