In a method for exchanging packets between first and second nodes of a packet-switched network, each packet comprises two fields settable to an idle value or measurement value. The first node transmits to the second node first packets having a filed set to measurement value. Upon reception of each first packet, the second node transmits back to the first node a second packet having a field set to measurement value. Upon reception of each second packet, the first node transmits to the second node a third packet having another field set to measurement value. A packet loss measurement is calculated as a difference between the number of first packets and the number of third packets.

Systems, devices, and methods of classifying encrypted network communications. A Traffic Monitoring Unit operates to monitor network traffic, and to capture HTTPS-encrypted packets that are exchanged over an HTTPS connection between an end-user device and a web server. An HTTPS Traffic Classification Unit operates to detect discrete HTTPS-encrypted objects within that HTTPS connection, and to classify those discrete HTTPS-encrypted objects based on at least one of: a first Analysis Model that classifies HTTPS-encrypted objects based on a type of content that is represented in the HTTPS-encrypted object; a second Analysis Model that classifies HTTPS-encrypted objects based on a type of server-side application that is associated with the HTTPS-encrypted object. Each Analysis Model utilizes Machine Learning (ML), Deep Learning (DL), Artificial Intelligence (AI), or Statistical and Mathematical Analysis (SMA).

Systems, devices, and methods of classifying encrypted network communications. A Traffic Monitoring Unit operates to monitor network traffic, and to capture HTTPS-encrypted packets that are exchanged over an HTTPS connection between an end-user device and a web server. An HTTPS Traffic Classification Unit operates to detect discrete HTTPS-encrypted objects within that HTTPS connection, and to classify those discrete HTTPS-encrypted objects based on at least one of: a first Analysis Model that classifies HTTPS-encrypted objects based on a type of content that is represented in the HTTPS-encrypted object; a second Analysis Model that classifies HTTPS-encrypted objects based on a type of server-side application that is associated with the HTTPS-encrypted object. Each Analysis Model utilizes Machine Learning (ML), Deep Learning (DL), Artificial Intelligence (AI), or Statistical and Mathematical Analysis (SMA).

The subject matter described herein includes methods, systems, and computer readable media for proactive network testing. One method for proactive network testing includes receiving, by a test controller and via a network tap, at least one metric associated with live network traffic; determining, by the test controller and using the at least one metric and a threshold value associated with the at least one metric, that a network test is to be performed; configuring, by the test controller, a first test agent to execute the network test; and executing, by the first test agent, the network test.

The subject matter described herein includes methods, systems, and computer readable media for proactive network testing. One method for proactive network testing includes receiving, by a test controller and via a network tap, at least one metric associated with live network traffic; determining, by the test controller and using the at least one metric and a threshold value associated with the at least one metric, that a network test is to be performed; configuring, by the test controller, a first test agent to execute the network test; and executing, by the first test agent, the network test.

A traffic control and monitoring module includes a firewall operating in a container namespace that is configured to control and monitor traffic to and from a container in the container namespace. The traffic control and monitoring module reports detected traffic to a traffic flow reporting module operating in a host namespace of the host operating system. The traffic control and monitoring module obtains traffic flows associated with a plurality of containers in different container namespaces and reports the traffic flows to a segmentation policy. Based on the reported traffic flows, the segmentation server may update a segmentation policy to improve network security.

A traffic control and monitoring module includes a firewall operating in a container namespace that is configured to control and monitor traffic to and from a container in the container namespace. The traffic control and monitoring module reports detected traffic to a traffic flow reporting module operating in a host namespace of the host operating system. The traffic control and monitoring module obtains traffic flows associated with a plurality of containers in different container namespaces and reports the traffic flows to a segmentation policy. Based on the reported traffic flows, the segmentation server may update a segmentation policy to improve network security.

Systems and methods include detecting whether a monitored network has a unique configuration; responsive to the unique configuration, determining an ingress point for flow samples; and utilizing the determined ingress point for the flow samples to generate a traffic report for the monitored network. The unique configuration is an inter-Autonomous System (AS) option-C Virtual Private Network (VPN) network where control and data planes are asymmetric. The approach provides traffic projection based on the flow samples with the asymmetric flows.

Systems and methods include detecting whether a monitored network has a unique configuration; responsive to the unique configuration, determining an ingress point for flow samples; and utilizing the determined ingress point for the flow samples to generate a traffic report for the monitored network. The unique configuration is an inter-Autonomous System (AS) option-C Virtual Private Network (VPN) network where control and data planes are asymmetric. The approach provides traffic projection based on the flow samples with the asymmetric flows.

Embodiments are directed to verifying media stream quality for multiparty video conferences. A verification video may be generated based on verification goals for a video provided by a video service. A marker may be embedded in the verification video. A video conference may be established using video stations such that the video conference may be provided by a video service. The verification video may be streamed to a video input of each video station. The video may be streamed to a video output buffer of each video station such that the video provides a view of the video conference and such that the marker that corresponds to each video station may be included in the video. Video information may be captured from the video output buffer of the video stations. The video service may be classified based on the video information from each video station.