Abstract

<!-- /* Font Definitions */ @font-face {font-family:??; panose-1:2 1 6 0 3 1 1 1 1 1; mso-font-alt:SimSun; mso-font-charset:134; mso-generic-font-family:auto; mso-font-pitch:variable; mso-font-signature:3 135135232 16 0 262145 0;} @font-face {font-family:"\@??"; panose-1:2 1 6 0 3 1 1 1 1 1; mso-font-charset:134; mso-generic-font-family:auto; mso-font-pitch:variable; mso-font-signature:3 135135232 16 0 262145 0;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; text-align:justify; text-justify:inter-ideograph; mso-pagination:none; font-size:10.5pt; mso-bidi-font-size:12.0pt; font-family:"Times New Roman"; mso-fareast-font-family:??; mso-font-kerning:1.0pt;} /* Page Definitions */ @page {mso-page-border-surround-header:no; mso-page-border-surround-footer:no;} @page Section1 {size:612.0pt 792.0pt; margin:72.0pt 90.0pt 72.0pt 90.0pt; mso-header-margin:36.0pt; mso-footer-margin:36.0pt; mso-paper-source:0;} div.Section1 {page:Section1;} -->

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.

This work is licensed under a Creative Commons Attribution 4.0 License.