Abstract: A system and method for determining malicious content associated with a platform(s) are provided. The system may analyze one or more communications of users associated with a platform. The system may implement a machine learning model including training data pre-trained, trained in real-time, or periodically trained based on one or more behavior content items associated with one or more accounts associated with the platform and content determined as being malicious. The system may determine, by implementing the machine learning model, whether the at least one communication of the one or more communications include malicious content based on determining at least one score by the machine learning model. The system may block the malicious content from being sent to other users of the platform in response to determining that the at least one score denotes that the at least one communication includes the malicious content.

Abstract: This disclosure describes techniques that include collecting underlay flow data along with overlay flow data within a network and correlating the data to enable insights into network operation and performance. In one example, this disclosure describes a method that includes collecting flow data for a network having a plurality of network devices and a plurality of virtual networks established within the network; storing the flow data in a data store; receiving a request for information about a data flow, wherein the request for information specifies a source virtual network for the data flow and further specifies a destination virtual network for the data flow; and querying the data store with the specified source virtual network and the specified destination virtual network to identify, based on the stored flow data, one or more network devices that have processed at least one packet in the data flow.

Abstract: This disclosure describes techniques that include collecting underlay flow data along with overlay flow data within a network and correlating the data to enable insights into network operation and performance. In one example, this disclosure describes a method that includes collecting flow data for a network having a plurality of network devices and a plurality of virtual networks established within the network; storing the flow data in a data store; receiving a request for information about a data flow, wherein the request for information specifies a source virtual network for the data flow and further specifies a destination virtual network for the data flow; and querying the data store with the specified source virtual network and the specified destination virtual network to identify, based on the stored flow data, one or more network devices that have processed at least one packet in the data flow.

Abstract: This disclosure describes techniques that include collecting underlay flow data along with overlay flow data within a network and correlating the data to enable insights into network operation and performance. In one example, this disclosure describes a method that includes collecting flow data for a network having a plurality of network devices and a plurality of virtual networks established within the network; storing the flow data in a data store; receiving a request for information about a data flow, wherein the request for information specifies a source virtual network for the data flow and further specifies a destination virtual network for the data flow; and querying the data store with the specified source virtual network and the specified destination virtual network to identify, based on the stored flow data, one or more network devices that have processed at least one packet in the data flow.

Abstract: This disclosure describes techniques that include collecting underlay flow data along with overlay flow data within a network and correlating the data to enable insights into network operation and performance. In one example, this disclosure describes a method that includes collecting flow data for a network having a plurality of network devices and a plurality of virtual networks established within the network; storing the flow data in a data store; receiving a request for information about a data flow, wherein the request for information specifies a source virtual network for the data flow and further specifies a destination virtual network for the data flow; and querying the data store with the specified source virtual network and the specified destination virtual network to identify, based on the stored flow data, one or more network devices that have processed at least one packet in the data flow.

Abstract: This disclosure describes techniques that include collecting underlay flow data along with overlay flow data within a network and correlating the data to enable insights into network operation and performance. In one example, this disclosure describes a method that includes collecting flow data for a network having a plurality of network devices and a plurality of virtual networks established within the network; storing the flow data in a data store; receiving a request for information about a data flow, wherein the request for information specifies a source virtual network for the data flow and further specifies a destination virtual network for the data flow; and querying the data store with the specified source virtual network and the specified destination virtual network to identify, based on the stored flow data, one or more network devices that have processed at least one packet in the data flow.