Chi-Square Test for Anomaly Detection in XML Documents Using Negative Association Rules

Abstract

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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.