Abstract: A method of detecting the onset of a ransomware attack is presented. In an example embodiment, file backup metadata for each of a plurality of computing devices is accessed and analyzed to detect anomalous file backup activity of individual ones of the computing devices. A determination is made as to whether the detected anomalous file backup activity of at least some of the computing devices is correlated in time. File description metadata for each of the computing devices is also accessed and analyzed to identify files in the computing devices that are anomalous to other files in the computing devices. A determination whether a ransomware attack has begun is based on a determination that the detected anomalous file backup activity of at least some of the computing devices is correlated in time, as well as on the identified anomalous files.

Abstract: A method of detecting the onset of a ransomware attack is presented. In an example embodiment, file backup metadata for each of a plurality of computing devices is accessed and analyzed to detect anomalous file backup activity of individual ones of the computing devices. A determination is made as to whether the detected anomalous file backup activity of at least some of the computing devices is correlated in time. File description metadata for each of the computing devices is also accessed and analyzed to identify files in the computing devices that are anomalous to other files in the computing devices. A determination whether a ransomware attack has begun is based on a determination that the detected anomalous file backup activity of at least some of the computing devices is correlated in time, as well as on the identified anomalous files.

Abstract: A method of detecting the onset of a ransomware attack is presented. In an example embodiment, file backup metadata for each of a plurality of computing devices is accessed and analyzed to detect anomalous file backup activity of individual ones of the computing devices. A determination is made as to whether the detected anomalous file backup activity of at least some of the computing devices is correlated in time. File description metadata for each of the computing devices is also accessed and analyzed to identify files in the computing devices that are anomalous to other files in the computing devices. A determination whether a ransomware attack has begun is based on a determination that the detected anomalous file backup activity of at least some of the computing devices is correlated in time, as well as on the identified anomalous files.

Abstract: A method of detecting the onset of a ransomware attack is presented. In an example embodiment, file backup metadata for each of a plurality of computing devices is accessed and analyzed to detect anomalous file backup activity of individual ones of the computing devices. A determination is made as to whether the detected anomalous file backup activity of at least some of the computing devices is correlated in time. File description metadata for each of the computing devices is also accessed and analyzed to identify files in the computing devices that are anomalous to other files in the computing devices. A determination whether a ransomware attack has begun is based on a determination that the detected anomalous file backup activity of at least some of the computing devices is correlated in time, as well as on the identified anomalous files.

Abstract: A method of detecting the onset of a ransomware attack is presented. In an example embodiment, file backup metadata for each of a plurality of computing devices is accessed and analyzed to detect anomalous file backup activity of individual ones of the computing devices. A determination is made as to whether the detected anomalous file backup activity of at least some of the computing devices is correlated in time. File description metadata for each of the computing devices is also accessed and analyzed to identify files in the computing devices that are anomalous to other files in the computing devices. A determination whether a ransomware attack has begun is based on a determination that the detected anomalous file backup activity of at least some of the computing devices is correlated in time, as well as on the identified anomalous files.

Abstract: Disclosed in some examples are systems, methods, and machine readable mediums for identifying insider threats by determining file system element activity models that correlate to undesirable behavior and then utilizing the determined model to detect insider threats. Events involving file system elements of a client computing device (e.g., a network endpoint) may be monitored by a file system element monitoring application on the client computing device. The values of these signals are aggregated across all events of the same type that have occurred within a predetermined time window (e.g., an hour) for a particular client computing device. Each time an aggregated signal has a value over the threshold, an anomaly is recorded. Anomaly counts for each signal are then calculated as the aggregate number of anomalies for a particular signal over a second time period, the span of which is determined by the generation of first anomaly to the close of an alert by the network monitor.

Abstract: A method of detecting the onset of a ransomware attack is presented. In an example embodiment, file backup metadata for each of a plurality of computing devices is accessed and analyzed to detect anomalous file backup activity of individual ones of the computing devices. A determination is made as to whether the detected anomalous file backup activity of at least some of the computing devices is correlated in time. File description metadata for each of the computing devices is also accessed and analyzed to identify files in the computing devices that are anomalous to other files in the computing devices. A determination whether a ransomware attack has begun is based on a determination that the detected anomalous file backup activity of at least some of the computing devices is correlated in time, as well as on the identified anomalous files.

Abstract: Disclosed in some examples are systems, methods, and machine readable mediums for identifying insider threats by determining file system element activity models that correlate to undesirable behavior and then utilizing the determined model to detect insider threats. Events involving file system elements of a client computing device (e.g., a network endpoint) may be monitored by a file system element monitoring application on the client computing device. The values of these signals are aggregated across all events of the same type that have occurred within a predetermined time window (e.g., an hour) for a particular client computing device. Each time an aggregated signal has a value over the threshold, an anomaly is recorded. Anomaly counts for each signal are then calculated as the aggregate number of anomalies for a particular signal over a second time period, the span of which is determined by the generation of first anomaly to the close of an alert by the network monitor.

Abstract: A method of detecting the onset of a ransomware attack is presented. In an example embodiment, file backup metadata for each of a plurality of computing devices is accessed and analyzed to detect anomalous file backup activity of individual ones of the computing devices. A determination is made as to whether the detected anomalous file backup activity of at least some of the computing devices is correlated in time. File description metadata for each of the computing devices is also accessed and analyzed to identify files in the computing devices that are anomalous to other files in the computing devices. A determination whether a ransomware attack has begun is based on a determination that the detected anomalous file backup activity of at least some of the computing devices is correlated in time, as well as on the identified anomalous files.

Abstract: A transaction tax calculating system using a hybrid method of referring to tables providing rules for state and local tax brackets, rounding and exceptions and applying those rounding and bracket tax calculation rules in conjunction for rounding.

Abstract: A transaction tax compliance system having a transaction tax compliance processor which receives transaction information from selling/purchasing input systems and returns, stores, and reports the tax liabilities caused by the transaction event.

Abstract: A transaction tax compliance system having a transaction tax compliance processor which receives transaction information from selling/purchasing input systems and returns, stores, and reports the tax liabilities caused by the transaction event.