Abstract: Disclosed herein are system, method, and computer program product embodiments for providing an anomaly detection system. Some aspects of this disclosure include a method for detecting anomaly in a network device. The method includes determining one or more similarity values between a flow vector corresponding to a flow associated with the network device and one or more flow clusters associated with the network device. The method further includes determining a maximum similarity value as a maximum of the one or more similarity values and comparing the maximum similarity value to a threshold. The method also includes, in response to the maximum similarity value being equal to or greater than the threshold, updating a flow cluster associated with the maximum similarity value. The method also includes, in response to the maximum similarity measure being less than the threshold, detecting the anomaly in the network device.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing an anomaly detection system. Some aspects of this disclosure include a method for detecting anomaly in a network device. The method includes determining one or more similarity values between a flow vector corresponding to a flow associated with the network device and one or more flow clusters associated with the network device. The method further includes determining a maximum similarity value as a maximum of the one or more similarity values and comparing the maximum similarity value to a threshold. The method also includes, in response to the maximum similarity value being equal to or greater than the threshold, updating a flow cluster associated with the maximum similarity value. The method also includes, in response to the maximum similarity measure being less than the threshold, detecting the anomaly in the network device.

Abstract: Provided herein are systems and methods for determining relationships between events occurring in networks. Notifications describing events occurring in networks can be received and processed to determine groups of network event types. A root-cause network can be generated based on the events, with the nodes of the root-cause network representing different event types and the edges of the root-cause network indicating directional, causal relationships between the nodes. A received network event can be processed to determine potential causes of the received network event based on the root-cause network and other events received by the network.

Abstract: Provided herein are systems and methods for determining relationships between events occurring in networks. Notifications describing events occurring in networks can be received and processed to determine groups of network event types. A root-cause network can be generated based on the events, with the nodes of the root-cause network representing different event types and the edges of the root-cause network indicating directional, causal relationships between the nodes. A received network event can be processed to determine potential causes of the received network event based on the root-cause network and other events received by the network.

Abstract: Provided herein are systems and methods for determining relationships between events occurring in networks. Notifications describing events occurring in networks can be received and processed to determine groups of network event types. A root-cause network can be generated based on the events, with the nodes of the root-cause network representing different event types and the edges of the root-cause network indicating directional, causal relationships between the nodes. A received network event can be processed to determine potential causes of the received network event based on the root-cause network and other events received by the network.

Abstract: Provided herein are systems and methods for determining relationships between events occurring in networks. Notifications describing events occurring in networks can be received and processed to determine groups of network event types. A root-cause network can be generated based on the events, with the nodes of the root-cause network representing different event types and the edges of the root-cause network indicating directional, causal relationships between the nodes. A received network event can be processed to determine potential causes of the received network event based on the root-cause network and other events received by the network.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing an anomaly detection system. Some aspects of this disclosure include a method for detecting anomaly in a network device. The method includes determining one or more similarity values between a flow vector corresponding to a flow associated with the network device and one or more flow clusters associated with the network device. The method further includes determining a maximum similarity value as a maximum of the one or more similarity values and comparing the maximum similarity value to a threshold. The method also includes, in response to the maximum similarity value being equal to or greater than the threshold, updating a flow cluster associated with the maximum similarity value. The method also includes, in response to the maximum similarity measure being less than the threshold, detecting the anomaly in the network device.