Abstract: Anomaly and causation detection in computing environments are disclosed. An example method includes receiving an input stream of data instances for a time series, each of the data instances being time stamped and including at least one principle value and a set of categorical attributes; generating anomaly scores for each of the data instances over continuous time intervals; detecting a change in the anomaly scores over the continuous time intervals for the data instances; and identifying which of the set of categorical attributes of the data instances caused the change in the anomaly scores using a counterfactual analysis. The counterfactual analysis may comprise removing a portion of the data instances; regenerating the anomaly scores for each of the remaining data instances over the continuous time intervals; and if the anomaly scores are improved, identifying the portion as a cause of anomalous activity. Recommendations to remediate the cause may be generated.

Abstract: A system and method for the detection of irregularities, such as fraud or malware, running on a device, is disclosed. The system comprises a monitoring program for reviewing data relating to operation of the device, a device profile including data items relating to typical operation of the device generated from messages relating to the device; and an alert module for generating an alert on detection of unusual activity relating to the device.

Abstract: A system and method for the detection of irregularities, such as fraud or malware, running on a device, is disclosed. The system comprises a monitoring program for reviewing data relating to operation of the device, a device profile including data items relating to typical operation of the device generated from messages relating to the device; and an alert module for generating an alert on detection of unusual activity relating to the device.

Abstract: A system and method for detecting fraudulent activity in the execution of transactions is disclosed. The system comprises a monitoring device for reviewing data relating to execution of transactions, a transaction profile and an alert module. The transaction profile includes a plurality of historic data items relating to typical transactions, which can be compared with current execution of transactions to generate an alert by the alert module if unusual activity is determined.

Abstract: A method (200) and system (10) for analysing data is disclosed. One or more data records (30) are passed to a data analysis system (20). The data records (30) comprised a plurality of data items (35) and a first one of the data items is selected from the data items (35) in the data record (30). A statistical model (50) can be retrieved from a store (40) in a computer system (10) and the statistical model (50) used to detect abnormal results from the selected data item (35) and produce a data model (55). This statistical model (50) is stored with the data record in the data base (60).

Abstract: A system (10) and method for detecting fraudulent activity in the execution of transactions is disclosed. The system (10) comprises a monitoring device (60) for reviewing data relating to execution of transactions, a transaction profile (62) and an alert module (67). The transaction profile (62) includes a plurality of historic data items (66) relating to typical transactions, which can be compared with current execution of transactions to generate an alert by the alert module (67) if unusual activity is determined.

Abstract: A system and method for the detection of irregularities, such as fraud or malware (50), running on a device (10), is disclosed. The system comprises a monitoring program (60) for reviewing data relating to operation of the device (10), a device profile (62) including data items (66) relating to typical operation of the device generated from messages (70) relating to the device (10); and an alert module (67) for generating an alert (80) on detection of unusual activity relating to the device (10).

Abstract: The field of the disclosure relates generally to a method and system for analysing behaviour of a computer infrastructure and the displaying the behaviour of the computer infrastructure in a graphical manner. The system comprises an analytical engine connected to agents running on devices in the computer infrastructure and analysing continuous data and asynchronous data.

Abstract: A system and method for analyzing a computer infrastructure. The system and method analyses messages sent and received within the computer infrastructure to determine a system model and exception data.

Abstract: A system and method for analysing a computer infrastructure. The system and method analyses messages sent and received within the computer infrastructure to determine a system model and exception data.

Abstract: A system and method for managing data is provided. The system includes a network for interconnecting a plurality of computers. A data storage means is connected to the network to receive, store and transmit a plurality of files to and from the network. A plurality of computers is also connected to the network. Each computer is configured for originating and for receiving files. Each of the files has a unique identifier associated therewith. Each computer may retrieve a file from the data storage means using the unique identifier.