Abstract: An example method according to some embodiments includes receiving flow data for a packet traversing a network. The method continues by determining a source endpoint group and a destination endpoint group for the packet. The method continues by determining that a policy was utilized, the policy being applicable to the endpoint group. Finally, the method includes updating utilization data for the policy based on the flow data.

Abstract: A method includes analyzing, via a first capturing agent, packets processed in a first environment associated with a first host to yield first data. The method includes analyzing, via a second capturing agent, packets processed by a second environment associated with a second host to yield second data, collecting the first data and the second data at a collector to yield aggregated data, transmitting the aggregated data to an analysis engine which analyzes the aggregated data to yield an analysis. Based on the analysis, the method includes identifying first packet loss at the first environment and second packet loss at the second environment.

Abstract: An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.

Abstract: A method provides for receiving network traffic from a host having a host IP address and operating in a data center, and analyzing a malware tracker for IP addresses of hosts having been infected by a malware to yield an analysis. When the analysis indicates that the host IP address has been used to communicate with an external host infected by the malware to yield an indication, the method includes assigning a reputation score, based on the indication, to the host. The method can further include applying a conditional policy associated with using the host based on the reputation score. The reputation score can include a reduced reputation score from a previous reputation score for the host.

Abstract: A method includes capturing first data associated with a first packet flow originating from a first host using a first capture agent deployed at the first host to yield first flow data, capturing second data associated with a second packet flow originating from the first host from a second capture agent deployed on a second host to yield second flow data and comparing the first flow data and the second flow data to yield a difference. When the difference is above a threshold value, the method includes determining that the second packet flow was transmitted by a component that bypassed an operating stack of the first host or a packet capture agent at the device to yield a determination, detecting that hidden network traffic exists, and predicting a malware issue with the first host based on the determination.

Abstract: An example method according to some embodiments includes receiving flow data for a packet traversing a network. The method continues by determining a source endpoint group and a destination endpoint group for the packet. The method continues by determining that a policy was utilized, the policy being applicable to the endpoint group. Finally, the method includes updating utilization data for the policy based on the flow data.

Abstract: An application and network analytics platform can capture telemetry from servers and network devices operating within a network. The application and network analytics platform can determine an application dependency map (ADM) for an application executing in the network. Using the ADM, the application and network analytics platform can resolve flows into flowlets of various granularities, and determine baseline metrics for the flowlets. The baseline metrics can include transmission times, processing times, and/or data sizes for the flowlets. The application and network analytics platform can compare new flowlets against the baselines to assess availability, load, latency, and other performance metrics for the application. In some implementations, the application and network analytics platform can automate remediation of unavailability, load, latency, and other application performance issues.

Abstract: An application and network analytics platform can capture telemetry from servers and network devices operating within a network. The application and network analytics platform can determine an application dependency map (ADM) for an application executing in the network. Using the ADM, the application and network analytics platform can resolve flows into flowlets of various granularities, and determine baseline metrics for the flowlets. The baseline metrics can include transmission times, processing times, and/or data sizes for the flowlets. The application and network analytics platform can compare new flowlets against the baselines to assess availability, load, latency, and other performance metrics for the application. In some implementations, the application and network analytics platform can automate remediation of unavailability, load, latency, and other application performance issues.

Abstract: An application and network analytics platform can capture telemetry from servers and network devices operating within a network. The application and network analytics platform can determine an application dependency map (ADM) for an application executing in the network. Using the ADM, the application and network analytics platform can resolve flows into flowlets of various granularities, and determine baseline metrics for the flowlets. The baseline metrics can include transmission times, processing times, and/or data sizes for the flowlets. The application and network analytics platform can compare new flowlets against the baselines to assess availability, load, latency, and other performance metrics for the application. In some implementations, the application and network analytics platform can automate remediation of unavailability, load, latency, and other application performance issues.

Abstract: An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.

Abstract: A method includes capturing first data associated with a first packet flow originating from a first host using a first capture agent deployed at the first host to yield first flow data, capturing second data associated with a second packet flow originating from the first host from a second capture agent deployed outside of the first host to yield second flow data and comparing the first flow data and the second flow data to yield a difference. When the difference is above a threshold value, the method includes determining that a hidden process exists and corrective action can be taken.

Abstract: This disclosure generally relate to a method and system for network policy simulation in a distributed computing system. The present technology relates techniques that enable simulation of a new network policy with regard to its effects on the network data flow. By enabling a simulation data flow that is parallel and independent from the regular data flow, the present technology can provide optimized network security management with improved efficiency.

Abstract: A method includes capturing first data associated with a first packet flow originating from a first host using a first capture agent deployed at the first host to yield first flow data, capturing second data associated with a second packet flow originating from the first host from a second capture agent deployed on a second host to yield second flow data and comparing the first flow data and the second flow data to yield a difference. When the difference is above a threshold value, the method includes determining that the second packet flow was transmitted by a component that bypassed an operating stack of the first host or a packet capture agent at the device to yield a determination, detecting that hidden network traffic exists, and predicting a malware issue with the first host based on the determination.

Abstract: An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.

Abstract: Systems, methods, and computer-readable media for hierarchichal sharding of flows from sensors to collectors. A first collector can receive a first portion of a network flow from a first capturing agent and determine that a second portion of the network flow was not received from the first capturing agent. The first collector can then send the first portion of the network flow to a second collector. A third collector can receive the second portion of the network flow from a second capturing agent and determine that the third collector did not receive the first portion of the network flow. The third collector can then send the second portion of the network flow to the second collector. The second collector can then aggregate the first portion and second portion of the network flow to yield the entire portion of the network flow.

Abstract: A method includes capturing first data associated with a first packet flow originating from a first host using a first capture agent deployed at the first host to yield first flow data, capturing second data associated with a second packet flow originating from the first host from a second capture agent deployed on a second host to yield second flow data and comparing the first flow data and the second flow data to yield a difference. When the difference is above a threshold value, the method includes determining that the second packet flow was transmitted by a component that bypassed an operating stack of the first host or a packet capture agent at the device to yield a determination, detecting that hidden network traffic exists, and predicting a malware issue with the first host based on the determination.

Abstract: Systems, methods, and computer-readable media for identifying bogon addresses. A system can obtain an indication of address spaces in a network. The indication can be based on route advertisements transmitted by routers associated with the network. The system can receive a report generated by a capturing agent deployed on a host. The report can identify a flow captured by the capturing agent at the host. The system can identify a network address associated with the flow and, based on the indication of address spaces, the system can determine whether the network address is within the address spaces in the network. When the network address is not within the address spaces in the network, the system can determine that the network address is a bogon address. When the network address is within the address spaces in the network, the system can determine that the network address is not a bogon address.

Abstract: An application and network analytics platform can capture telemetry from servers and network devices operating within a network. The application and network analytics platform can determine an application dependency map (ADM) for an application executing in the network. Using the ADM, the application and network analytics platform can resolve flows into flowlets of various granularities, and determine baseline metrics for the flowlets. The baseline metrics can include transmission times, processing times, and/or data sizes for the flowlets. The application and network analytics platform can compare new flowlets against the baselines to assess availability, load, latency, and other performance metrics for the application. In some implementations, the application and network analytics platform can automate remediation of unavailability, load, latency, and other application performance issues.

Abstract: Systems, methods, and computer-readable media for identifying bogon addresses. A system can obtain an indication of address spaces in a network. The indication can be based on route advertisements transmitted by routers associated with the network. The system can receive a report generated by a capturing agent deployed on a host. The report can identify a flow captured by the capturing agent at the host. The system can identify a network address associated with the flow and, based on the indication of address spaces, the system can determine whether the network address is within the address spaces in the network. When the network address is not within the address spaces in the network, the system can determine that the network address is a bogon address. When the network address is within the address spaces in the network, the system can determine that the network address is not a bogon address.

Abstract: A network analytics system can receive first sensor data, including first network activity and a first timestamp associated with a first clock of a first node, and second sensor data, including second network activity and a second timestamp associated with a second clock of a second node. The system can determine a first delta between the first clock and a third clock based on the first timestamp, and a second delta between the second clock and the third clock. The system can determine a first communication latency associated with a first sensor of the first node, and a second communication latency associated with a second sensor of the second node. The system can generate a report that synchronizes one or more data flows between the first node and the second node based on the first delta, the second delta, the first communication latency, and the second communication latency.