Abstract: A system for identifying and associating merchant attributes data of a merchant with transaction data may be integrated with existing payment processing systems to provide for detailed merchant attributes data (e.g., merchant name, merchant ownership data, and merchant address data). The system may receive and modify merchant attributes data retrieved from an external source (e.g., an acquirer computer or third party to a transaction) and store the merchant attributes data in a database. The merchant attributes data may be retrieved and correlated with transaction data and provided to entities (e.g., issuer computers) involved with the transaction for authorization decisioning, fraud analysis, and data analytics.

Abstract: A system for identifying and associating merchant attributes data of a merchant with transaction data may be integrated with existing payment processing systems to provide for detailed merchant attributes data (e.g., merchant name, merchant ownership data, and merchant address data). The system may receive and modify merchant attributes data retrieved from an external source (e.g., an acquirer computer or third party to a transaction) and store the merchant attributes data in a database. The merchant attributes data may be retrieved and correlated with transaction data and provided to entities (e.g., issuer computers) involved with the transaction for authorization decisioning, fraud analysis, and data analytics.

Abstract: A system for identifying and associating merchant attributes data of a merchant with transaction data may be integrated with existing payment processing systems to provide for detailed merchant attributes data (e.g., merchant name, merchant ownership data, and merchant address data). The system may receive and modify merchant attributes data retrieved from an external source (e.g., an acquirer computer or third party to a transaction) and store the merchant attributes data in a database. The merchant attributes data may be retrieved and correlated with transaction data and provided to entities (e.g., issuer computers) involved with the transaction for authorization decisioning, fraud analysis, and data analytics.

Abstract: Detecting harmful or illegal intrusions into a computer network or into restricted portions of a computer network uses a process of synthesizing anomalous data to be used in training a neural network-based model for use in a computer network intrusion detection system. Anomalous data for artificially creating a set of features reflecting anomalous behavior for a particular activity is performed. This is done in conjunction with the creation of normal-behavior feature values. A distribution of users of normal feature values and an expected distribution of users of anomalous feature values are then defined in the form of histograms. The anomalous-feature histogram is then sampled to produce anomalous-behavior feature values. These values are then used to train a model having a neural network training algorithm where the model is used in the computer network intrusion detection system.

Abstract: Detecting harmful or illegal intrusions into a computer network or into restricted portions of a computer network uses a process of synthesizing anomalous data to be used in training a neural network-based model for use in a computer network intrusion detection system. Anomalous data for artificially creating a set of features reflecting anomalous behavior for a particular activity is performed. This is done in conjunction with the creation of normal-behavior feature values. A distribution of users of normal feature values and an expected distribution of users of anomalous feature values are then defined in the form of histograms. The anomalous-feature histogram is then sampled to produce anomalous-behavior feature values. These values are then used to train a model having a neural network training algorithm where the model is used in the computer network intrusion detection system.

Abstract: Detecting harmful or illegal intrusions into a computer network or into restricted portions of a computer network uses a process of synthesizing anomalous data to be used in training a neural network-based model for use in a computer network intrusion detection system. Anomalous data for artificially creating a set of features reflecting anomalous behavior for a particular activity is performed. This is done in conjunction with the creation of normal-behavior feature values. A distribution of users of normal feature values and an expected distribution of users of anomalous feature values are then defined in the form of histograms. The anomalous-feature histogram is then sampled to produce anomalous-behavior feature values. These values are then used to train a model having a neural network training algorithm where the model is used in the computer network intrusion detection system.

Abstract: Detecting harmful or illegal intrusions into a computer network or into restricted portions of a computer network uses a features generator or builder to generate a feature reflecting changes in user and user group behavior over time. User and user group historical means and standard deviations are used to generate a feature that is not dependent on rigid or static rule sets. These statistical and historical values are calculated by accessing user activity data listing activities performed by users on the computer system. Historical information is then calculated based on the activities performed by users on the computer system. The feature is calculated using the historical information based on the user or group of users activities. The feature is then utilized by a model to obtain a value or score which indicates the likelihood of an intrusion into the computer network. The historical values are adjusted according to shifts in normal behavior of users of the computer system.