Abstract: Techniques are disclosed for passively characterizing a type of host or computing device which may be engaged in a transaction between the host and another computing device. Observation data corresponding to one or more sessions of network traffic between an unclassified host and a second system may be passively generated by a device characterization server. The observation data can be processed by the device characterization server using a machine-learning classifier. The machine-learning classifier can be trained with a set of training data that includes multiple sessions of network traffic from multiple training data hosts. Each session of network traffic includes an exchange of multiple packets in various embodiments, including packets sent from, and packets received by, the training data hosts. Based on the processing, the unclassified host may be characterized by the device characterization server as one of a physical computing device, a virtual machine, or a container.

Abstract: Techniques are disclosed for passively characterizing a type of host or computing device which may be engaged in a transaction between the host and another computing device. Observation data corresponding to one or more sessions of network traffic between an unclassified host and a second system may be passively generated by a device characterization server. The observation data can be processed by the device characterization server using a machine-learning classifier. The machine-learning classifier can be trained with a set of training data that includes multiple sessions of network traffic from multiple training data hosts. Each session of network traffic includes an exchange of multiple packets in various embodiments, including packets sent from, and packets received by, the training data hosts. Based on the processing, the unclassified host may be characterized by the device characterization server as one of a physical computing device, a virtual machine, or a container.

Abstract: Techniques are disclosed for passively characterizing a type of host or computing device which may be engaged in a transaction between the host and another computing device. Observation data corresponding to one or more sessions of network traffic between an unclassified host and a second system may be passively generated by a device characterization server. The observation data can be processed by the device characterization server using a machine-learning classifier. The machine-learning classifier can be trained with a set of training data that includes multiple sessions of network traffic from multiple training data hosts. Each session of network traffic includes an exchange of multiple packets in various embodiments, including packets sent from, and packets received by, the training data hosts. Based on the processing, the unclassified host may be characterized by the device characterization server as one of a physical computing device, a virtual machine, or a container.

Abstract: Techniques are disclosed for passively characterizing a type of host or computing device which may be engaged in a transaction between the host and another computing device. Observation data corresponding to one or more sessions of network traffic between an unclassified host and a second system may be passively generated by a device characterization server. The observation data can be processed by the device characterization server using a machine-learning classifier. The machine-learning classifier can be trained with a set of training data that includes multiple sessions of network traffic from multiple training data hosts. Each session of network traffic includes an exchange of multiple packets in various embodiments, including packets sent from, and packets received by, the training data hosts. Based on the processing, the unclassified host may be characterized by the device characterization server as one of a physical computing device, a virtual machine, or a container.