文章基本信息

标题：Chi-Square Test for Anomaly Detection in XML Documents Using Negative Association Rules
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作者：K. Premalatha ; A.M. Natarajan
期刊名称：Computer and Information Science
印刷版ISSN：1913-8989
电子版ISSN：1913-8997
出版年度：2009
卷号：2
期号：1
页码：35
DOI：10.5539/cis.v2n1P35
出版社：Canadian Center of Science and Education
摘要：Normal 0 7.8 ? 0 2 false false false MicrosoftInternetExplorer4  /* Style Definitions */ table.MsoNormalTable {mso-style-name:????; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} Anomaly detection is the double purpose of discovering interesting exceptions and identifying incorrect data in huge amounts of data.   Since anomalies are rare events, which violate the frequent relationships among data. Normally anomaly detection builds models of normal behavior and automatically detects significant deviations from it.   The proposed system detects the anomalies in nested XML documents by independency between data.   The negative association rules and the chi-square test for independency are applied on the data and a model of abnormal behavior is built as a signature profile.   This signature profile can be used to identify the anomalies in the system. The proposed system limits the unnecessary rules for detecting anomalies.