Abstract: A method and system for classifying a device accessing a computer network. The method including: providing a framework of models configured to classify the device; reviewing a network traffic flow associated with a device; extracting flow attributes associated with a network traffic flow; deriving further flow attributes based on the extracted flow attributes; determining at least one model of the framework of models based on the derived flow attributes and extracted encrypted flow attributes; and classifying the device associated with the network traffic flow based on the at least one model. The system includes: a learning engine configured to provide a framework of models; a packet processing engine configured to review a network traffic flow associated with a device; a device classification engine configured to extract flow attributes, derive further flow attributes and determine at least one model; and a device information aggregator configured to classify the device.

Abstract: A method for classifying tunneled network traffic including: providing at least one model configured to classify network traffic; retrieving a plurality of packets from a traffic flow; determining input and output statistics of the traffic flow based on the plurality of packets; and classifying, via the at least one model, the traffic flow based on the input and output statistics. A system for classifying tunneled network traffic including: a model making module configured to provide at least one model configured to classify network traffic; a packet processing engine configured to retrieve a plurality of packets from a traffic flow; a data collection module configured to determine input and output statistics of the traffic flow based on the plurality of packets; and a classification module configured to classify, via the at least one model, the traffic flow based on the input and output statistics.

Abstract: A system and method for classifying network traffic including: providing a framework of models configured to classify network traffic; determining, via a packet processing engine, whether the network traffic flow is encrypted; if the network traffic flow is encrypted: extracting, via the packet processing engine, encrypted flow attributes associated with a network traffic flow; deriving further flow attributes based on the extracted encrypted flow attributes; determining at least one model of the framework of models based on the derived flow attributes and extracted encrypted flow attributes; and classifying, via a traffic classification engine, the traffic flow as a category of traffic based on the at least one model; and if the network traffic flow is not encrypted: extracting non-encrypted flow attributes associated with a network traffic flow; classifying the traffic flow as a category of traffic based on the non-encrypted flow attributes.

Abstract: A system and method for classifying network traffic including: providing a framework of models configured to classify network traffic; determining, via a packet processing engine, whether the network traffic flow is encrypted; if the network traffic flow is encrypted: extracting, via the packet processing engine, encrypted flow attributes associated with a network traffic flow; deriving further flow attributes based on the extracted encrypted flow attributes; determining at least one model of the framework of models based on the derived flow attributes and extracted encrypted flow attributes; and classifying, via a traffic classification engine, the traffic flow as a category of traffic based on the at least one model; and if the network traffic flow is not encrypted: extracting non-encrypted flow attributes associated with a network traffic flow; classifying the traffic flow as a category of traffic based on the non-encrypted flow attributes.

Abstract: A method and system for classifying a device accessing a computer network. The method including: providing a framework of models configured to classify the device; reviewing a network traffic flow associated with a device; extracting flow attributes associated with a network traffic flow; deriving further flow attributes based on the extracted flow attributes; determining at least one model of the framework of models based on the derived flow attributes and extracted encrypted flow attributes; and classifying the device associated with the network traffic flow based on the at least one model. The system includes: a learning engine configured to provide a framework of models; a packet processing engine configured to review a network traffic flow associated with a device; a device classification engine configured to extract flow attributes, derive further flow attributes and determine at least one model; and a device information aggregator configured to classify the device.

Abstract: A system and method for classifying network traffic including: providing a framework of models configured to classify network traffic; determining, via a packet processing engine, whether the network traffic flow is encrypted; if the network traffic flow is encrypted: extracting, via the packet processing engine, encrypted flow attributes associated with a network traffic flow; deriving further flow attributes based on the extracted encrypted flow attributes; determining at least one model of the framework of models based on the derived flow attributes and extracted encrypted flow attributes; and classifying, via a traffic classification engine, the traffic flow as a category of traffic based on the at least one model; and if the network traffic flow is not encrypted: extracting non-encrypted flow attributes associated with a network traffic flow; classifying the traffic flow as a category of traffic based on the non-encrypted flow attributes.

Abstract: A method for heuristic control of traffic management on a computer network, the method including: setting predetermined benchmarks for traffic; performing a traffic management control loop to determine at least one value of a quality of experience (QoE) metric, via a QoE measuring module, on the network based on the traffic flow; performing a heuristic control loop comprising: aggregating the at least one value of the QoE metric obtained from the traffic management control loop; determining a new benchmark based on the aggregation of the QoE metric, via a heuristic control module; and sending the new benchmark to the QoE measuring module.

Abstract: A method for heuristic control of traffic management on a computer network, the method including: setting predetermined benchmarks for traffic; performing a traffic management control loop to determine at least one value of a quality of experience (QoE) metric, via a QoE measuring module, on the network based on the traffic flow; performing a heuristic control loop comprising: aggregating the at least one value of the QoE metric obtained from the traffic management control loop; determining a new benchmark based on the aggregation of the QoE metric, via a heuristic control module; and sending the new benchmark to the QoE measuring module.