Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.