Using SQL Hotspots in a Prioritization Heuristic for Detecting All Types of Web Application Vulnerabilities: Difference between revisions
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A predictive model for classifying components as being either vulnerable or neutral will make either correct or incorrect classifications. As such, for a given test of the model, there are true positives, where the model correctly classifies a component as vulnerable, and true negatives, where the model correctly classifies the component as neutral. When the model is wrong, there are false positives, where the model classifies the component as being vulnerable, but the component was neutral, and false negatives where the model failed to identify a vulnerable component. The performance of a given model to classify components as being one of two binary options has often been evaluated using two measurements: precision and recall<sup>[10]</sup>. | A predictive model for classifying components as being either vulnerable or neutral will make either correct or incorrect classifications. As such, for a given test of the model, there are true positives, where the model correctly classifies a component as vulnerable, and true negatives, where the model correctly classifies the component as neutral. When the model is wrong, there are false positives, where the model classifies the component as being vulnerable, but the component was neutral, and false negatives where the model failed to identify a vulnerable component. The performance of a given model to classify components as being one of two binary options has often been evaluated using two measurements: precision and recall<sup>[10]</sup>. | ||
Precision is defined in Equation 1, where '''tp''' is the number of true positives identified by the model, and '''fp''' is the number of false positives identified by the model. Precision can be viewed as a measure of exactness that a model exhibits. | |||
== 3. Related Work == | == 3. Related Work == | ||