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 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 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: 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 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 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 can include choosing a pattern or patterns of network traffic. This pattern can be representative of a certain type of traffic such as an attack. The pattern can be associated with various components of a network and can describe expected behavior of these various components. A system performing this method can then choose a nodes or nodes to generate traffic according to the pattern and send an instruction accordingly. After this synthetic traffic is generated, the system can compare the behavior of the components with the expected behavior. An alert can then be created to notify an administrator or otherwise remedy any problems.

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: 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: 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.

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.

Abstract: Managing a network environment to identify spoofed packets is disclosed. A method includes analyzing, via a first capture agent, packets processed by a first environment in a network associated with a first host, and analyzing, via a second capture agent, packets processed by a second environment in the network associated with a second host. The method includes collecting the first data and the second data at a collector and generating a topological map of the network and a history of network activity associated with the first environment and the second environment. The method includes extracting network data from a packet and comparing the extracted network data with stored network data in the database. When the comparison indicates that the extracted network data does not match the stored network data (i.e., the reported source does not match an expected source for the packet), determining that the packet is a spoofed packet.

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.