Abstract: Apparatus and methods for intelligently classifying unclassified transactions are provided. A program may receive a set of transaction data. The program may divide the set into a training set of classified transactions and a test set of unclassified transactions. The program may analyze the training set to generate a document term matrix. The program may analyze the test set and compare the analyzed terms to the document term matrix to predict a classification. The program may iterate to a pre-determined level of confidence in the prediction and assign a classification to a particular transaction.

Abstract: Systems and methods are provided for transforming historical data collected in response to one or more triggering events, in order to classify textual values. Embodiments access a plurality of textual values from historical transaction data; identify one or more distinct patterns within the plurality of textual values; group the textual values based on the one or more distinct patterns, thereby forming one or more clusters; apply a similarity gauge to the textual values of each of the clusters to determine similarity or dissimilarity among the textual values of each cluster; and filter the textual values of each cluster to determine which textual values belong in each cluster, wherein the textual values that belong are cluster values. Some embodiments also remove undesired characters from the textual values, and in some cases identifying the distinct patterns includes comparing pronunciations and/or phonetics of the textual values.

Abstract: Systems and methods are provided for transforming historical data collected in response to one or more triggering events, in order to classify textual values. Embodiments access a plurality of textual values from historical transaction data; identify one or more distinct patterns within the plurality of textual values; group the textual values based on the one or more distinct patterns, thereby forming one or more clusters; apply a similarity gauge to the textual values of each of the clusters to determine similarity or dissimilarity among the textual values of each cluster; and filter the textual values of each cluster to determine which textual values belong in each cluster, wherein the textual values that belong are cluster values. Some embodiments also remove undesired characters from the textual values, and in some cases identifying the distinct patterns includes comparing pronunciations and/or phonetics of the textual values.

Abstract: A computer system assesses the overall risk for different technologies for an organization. Technologies may be evaluated by obtaining severity levels and environmental risk scores for the vulnerabilities associated with the technologies. Each severity level measures a possible risk level of a corresponding vulnerability, while each environmental risk score is based on the organization's environment. Technology risk scores are then determined from the severity levels and the environmental risk scores. Each technology may then be categorized from a statistical distribution of the technology risk scores. An indexed risk score for each technology may also be determined based on time trending variables. Inputs may be a number of vulnerabilities, blended advisory/severity scores, and a standard deviation of the blended advisory/severity scores, and the results then provide behavior forecasting of the technologies.