Nec Corporation

Giuseppe Siracusano

Roberto Bifulco

Roberto Gonzalez Sanchez

Davide Sanvito

A method for learning vector representations of network traffic data offline includes: receiving historical network traffic data and a domain classification list; generating a unique domain names list based on the historical network traffic data; generating a bad domains list based on the unique domain names list and the domain classification list; modifying the unique domain names list by replacing each entry of the unique domain names list that appears in the bad domains list by a common classification label; and learning a respective vector representation for each entry of the modified unique domain names list.

Method and system for preventive filtering of network traffic

For providing an efficient network use and resource allocation within the network a method for operating a network is provided, wherein user network traffic is controlled by an operator, comprising the following steps: a) monitoring user network traffic data on a per user basis, b) using said network traffic data in a learning process for providing a prediction of user network traffic on a per user basis, and c) controlling user network traffic under consideration of said prediction, including allocating network resources under consideration of said prediction to one or more users, preferably for providing a definable Quality of Service, QoS, per at least ne of said one or more users and/or per at least one other user. Further, a corresponding network is claimed.

Method for operating a network

A method for executing a binarized neural network (BNN) using a switching chip includes describing an artificial neural network application in a binarized form to provide the BNN; configuring a parser of the switching chip to encode an input vector of the BNN in a packet header; configuring a plurality of match-action tables (MATs) of the switching chip to execute, on the input vector encoded in the packet header, one or more of the operations including XNOR, bit counting, and sign operations such that the plurality of MATs are configured to: implement a bitwise XNOR operation between the input vector and a weights matrix to produce a plurality of first stage vectors, implement an algorithm for counting a number of bits set to 1 in the plurality of first stage vectors to produce a plurality of second stage vectors, and implement a sign operation on the second stage vectors.

Using programmable switching chips as artificial neural networks engines

A method for service function chaining in a network includes defining, for a flow of packets, a chain of selected network service functions (NSFs) to be traversed by the flow. Each of the selected NSFs is associated with a programmable switch. The method also includes generating a chain establishment packet (CEP) that contains network identifier information (NII) about the selected NSFs and that is configured as a regular network packet to be delivered to the destination node along a network path that includes the programmable switches to which the selected NSFs is associated. Each programmable switch, upon receipt of the CEP and based on the NII about the selected NSFs contained in the CEP, performs installation of packet forwarding rules for the flow together with network address and port translation operations, and selects, on behalf of the respective NSF, socket parameters for use by the NSF for processing the flow.

Method and system for service function chaining

A method for classifying network traffic data includes: selecting a subset of network destinations from the network traffic data to be evaluated to determine whether to classify the subset of network destinations with a common classification label, the common classification label corresponding to a common classification class; determining a list of vector representations for the subset of the network destinations contained in the network traffic data and a vector representation for the common classification label; computing a distance between a vector representation for a network domain and the vector representation of the classification label, the vector representation for the network domain being determined from the list of vector representations; classifying the subset of the network destinations as belonging to the common classification class based on the distance being less than a predefined threshold.

A method for load balancing in a computer network includes receiving application information for an application and information relating to an artificial neural network (NN) computation to be executed by the application. A configuration is derived for one or more network devices based on the application information and the information relating to the NN computation. The configuration is installed in the one or more network devices such that at least one of the network devices on a path of a network packet performs a subset of the NN computation and encodes a result of the subset of the NN computation into a header of the network packet.

Growing community of inventors

Heidelberg, Germany

Giuseppe Siracusano