Abstract: A method for identifying a threatening network comprises an asymmetric threat signature (AT-SIG) algorithm comprising a network movement before/after algorithm that provides a graphical plot of changes in network transaction activity from before to after a specified time and further comprising one or more of: a network progression algorithm that provides a graphical plot to analyze behavior in small increments of time without specification or emphasis upon a particular time or event; a statistical network anomaly ranking algorithm that provides as output a ranked list of the networks; and an anomaly trend graphs algorithm that analyzes and visualizes the networks' anomaly scores over time. Also disclosed are an AT-SIG system and a software program product.

Abstract: A system and method for identifying a threatening network is provided. The system comprises a network movement before/after algorithm that provides a graphical plot of changes in networks' communications activity from before to after a key event occurs, so that an analyst is able to identify anomalous behavior; a network progression algorithm that provides a graphical plot to analyze behavior in small increments of time without specification or emphasis upon a particular event, so that the analyst is able to see a trend in behavioral changes; a statistical network anomaly ranking algorithm that provides as output a ranked list of the networks; and an anomaly trend graphs algorithm that analyzes and visualizes the networks' anomaly scores over time, so that the analyst is able to see which networks are consistently suspicious, which networks accumulate more suspiciousness in response to an event, and which networks are trending toward more suspiciousness.

Abstract: A system and method for identifying a threatening network is provided. The system comprises a network movement before/after algorithm that provides a graphical plot of changes in networks' communications activity from before to after a key event occurs, so that an analyst is able to identify anomalous behavior; a network progression algorithm that provides a graphical plot to analyze behavior in small increments of time without specification or emphasis upon a particular event, so that the analyst is able to see a trend in behavioral changes; a statistical network anomaly ranking algorithm that provides as output a ranked list of the networks; and an anomaly trend graphs algorithm that analyzes and visualizes the networks' anomaly scores over time, so that the analyst is able to see which networks are consistently suspicious, which networks accumulate more suspiciousness in response to an event, and which networks are trending toward more suspiciousness.