300 results about "Network traffic control" patented technology

A method of tracking network traffic anomalies in a computing system, comprises receiving an ingress network packet at a configurable logic device (CLD), associating a timestamp with the packet, identifying at least one anomaly based on the contents of the packet, and storing the anomalous packet and the timestamp in a persistent memory.

The present invention is a computer system and a method for gathering, processing and analysis of network information resulting in presentation and visualization of packet networks in the form of individual virtual flows, sometimes also called connections or sessions, containing their statistical characteristics in a time-sampled dynamics. The system, deployed as a separate device or co-hosted with other network devices, collects and processes information from all valid packets in network, classifies and maps gathered statistics to the statistics of relevant virtual flows. The statistical information is further processed by the system to provide near-real presentation, as well as stored in a searchable database for future analyses. The invention to be used by network engineers and administrators as a tool for a near real-time control of network traffic, as an analytical tool for solving network bottlenecks, network performance optimization and troubleshooting analyses, cutting costs by optimizing network layout, appropriate organization of traffic and intelligent configuration of QoS, routers and other network devices.

A network traffic monitoring system for redirecting network traffic between a client device and a cloud service includes a monitor proxy server configured as a network intermediary between the client device and a federated identity provider and between the client device and the cloud service. The monitor proxy server is configured to receive a redirect response generated by the federated identity provider upon user authentication. The monitor proxy server is configured to rewrite the redirect web address to the web address of the monitor proxy server. The monitor proxy server is further configured to rewrite a response web address in network communications between the cloud service and the client device to the web address of the monitor proxy server. As a result, network traffic between the cloud service and the client device is routed through the monitor proxy server after user authentication by the federated identity provider.

Various embodiments of the present invention set forth techniques for security monitoring of a network connection, including analyzing network traffic data for a network connection associated with a computing device, identifying one or more network traffic metrics for the network connection based on the network traffic data, determining that the network connection corresponds to at least one network connection profile based on the one or more network traffic metrics, detecting a potential security threat for the network connection based on the one or more network traffic metrics and the at least one network connection profile, and initiating a mitigation action with respect to the network connection in response to detecting the potential security threat. Advantageously, the techniques allow detecting potential security threats based on network traffic metrics and categorizations, without requiring monitoring of the content or the total volume of all traffic exchanged via the connection.

The invention relates to a method for detection and prediction of incidents and traffic queues formed by overloading. This is done in real time with use of sensors in a road network. Predictions are used also to reach a faster and more reliable detection. Sensor measurements are also used in the process, where the comparison with expected values are used for successively updating stored parameter values for the involved algorithms. By this, the system can succeedingly adapt itself for changed situations. The strong traffic variations, that are naturally occurring at short time intervals are treated with the use of noise-based methods. By this, there are formed distribution related measures as e.g. the standard deviation, which can be estimated from measurements, and submit a base for estimating probabilities for deviations of a certain size, e.g. related to the standard deviation. Automatic incident detection (AID) is based on determination of the desired false-alarm rate, and the related threshold level. The method includes accumulated measurements. Faster and more reliable incident detections are received with the use of the invented prediction process method.

The invention provides an OpenFlow network flow control method for the QoS assurance of an application. The OpenFlow network flow control method comprises the following steps that: a user requests a service from a server; the server sends a QoS request message to an SDN controller; after receiving the message, the controller calculates an optimal transmission path for the server according to a network resource use condition and reserves a resource; and simultaneously, the transmission state and the network link condition of the server are monitored when the server transmits a QoS data flow, such that a QoS flow is transmitted reliably and stably. A required network resource is provided for an application service by strictly assuring the QoS transmission of the application service and utilizing the service difference to network resource requirements; therefore, the QoS of the application is effectively ensured; and simultaneously, the network resource is utilized effectively.

A computer-implemented method for protecting a computer network (22) includes receiving at a gateway (24) data transmitted from a source address for delivery to a destination on the computer network. The data are encrypted at the gateway using an encryption key selected from a set of one or more keys that are not available to the source address. The encrypted data are transmitted over the computer network toward the destination. The transmitted encrypted data are received and decrypted for use at the destination by means of one of the keys in the set.

The invention discloses an intelligent public transportation system with unmanned driving and field return charging functions.The intelligent public transportation system comprises a management platform, a vehicle-mounted terminal, a network traffic control system, a user terminal, a charging station management terminal and a cloud data management terminal.The vehicle-mounted terminal comprises an unmanned intelligent driving system, an automatic charging electricity receiving system, a vehicle status monitoring system, an identity information recognition system and a charging system, all of which are connected with the management platform.The unmanned intelligent driving system and the vehicle status monitoring system are both connected with the cloud data management terminal.The identity information recognition system and the charging system are both connected with the user terminal, and the identity information recognition system is connected with the charging system.The charging station management terminal is connected with the cloud data management terminal.The electric vehicle technology and the unmanned driving technology are combined, a cloud data analysis and intelligent management system achieves a special vehicle use operating mode, and on the premise that safe vehicle use is guaranteed, an individual, intelligent and green public transportation means is provided for a user.

The invention discloses a network traffic abnormality detection method and system. The method comprises the steps of sampling network traffic, thereby obtaining time sequences composed of traffic data samples through sampling; extracting sample subsequences from the time sequences according to time windows of preset sizes; inputting the sample subsequences into a classification model for model training by taking the sample subsequences as input samples, thereby determining parameters of the classification model; and testing to obtain normal and abnormal classification results of the network traffic according to the classification model of which parameters are determined. According to the network traffic abnormality detection method and system provided by the invention, the extracted time sequence composed of the traffic data samples are taken as the samples for model training and classification detection, the fact that network traffic changes have continuity and correlation in time is taken into consideration, and time information is introduced into abnormal traffic detection and classification. Compared with the prior art, the method and the system have the advantage that the accuracy of carrying out abnormal behavior detection on the network traffic can be improved.

In a network traffic monitoring system and method for capturing a service distribution to monitor an IP network using Diffserv protocol, each IP packet on the network is monitored by extracting DSCP and IP addresses from a header of the packet. Then, traffic information is accumulated for each service class and for each sub network address, using the extracted information.

A debugging system running on a host device uses an embedded target-side network analyzer to acquire communications information such as packets. The target-side network analyzer logs and analyzes packets by obtaining data from the target's Internet Protocol (IP) stack when diagnostic functions are invoked at breakpoints. This allows display, logging, analysis and other uses of decoded network traffic. This approach can be used in applications (e.g., wireless or those using protocols such as RADIUS and PPP, etc.) where identifying and intercepting traffic to or from a specific target device may be difficult or impossible. With this approach developers can analyze network traffic in real time without any special hardware.

The embodiment of the invention discloses a network flow control method and a corresponding system and a device. The network flow control method comprises the steps that: a strategy center resolves flow information calculated by a DPI device according to the content of a received deep packet to calculate the total network flow, and formulates a corresponding flow control strategy according to the total network flow and sends the corresponding flow control strategy to the DPI device; the DPI device obtains a corresponding flow threshold value of the next predetermined period of time according to the flow control strategy; the DPI device controls the flow when the flow passing through the DPI device exceeds the flow threshold value. By the technical proposal provided by the embodiment of the invention, the strategy control center can formulate different flow control strategies according to the specific flow information calculated by the DPI device, and therefore selective control over service flow can be carried out, which has no effect on the normal service of users.

A mirror network flow control protocol based on a software definition in a virtual network environment is characterized in that the protocol can adapt a system structure of mirror flow collection, mirror flow distribution and mirror flow control, which is decoupling in functions and has distributed disposition. A mirror flow collector node/virtual machine is deployed in a business network environment of a user, and has a main function to capture the mirror flow in the virtual network environment and to carry out forwarding according to a target assigned by the protocol. A mirror flow dispatcher is deployed in a non-business network environment, that is the influence of a network load on the normal network communication of the user business network environment does not need to be considered, and the function of the mirror flow dispatcher is to carry out flow copy and distribution according to a multi-purpose flow analysis device assigned by the protocol. A mirror flow center controller uniformly controls forwarding logic of the whole mirror network flow, and provides soft defined interfaces.

The invention discloses a train tracking interval real-time early warning system based on CTC (central traffic control) and an early warning method, which belong to the field of high-speed train safety control technology. A dispatching center subsystem early warning server of the system consists of an early warning server and a dispatching command output unit, an application server of the dispatching center subsystem consists of a train number tracking unit and a wireless checking unit, vehicular equipment comprises a wireless train number vehicular encoder, and the system comprises a wireless train number decoder. The method includes acquiring a train number from decoded train comprehensive information by the aid of the wireless checking unit, comparing the acquired train number with a train number obtained by the train number tracking unit, and acquiring an accurate and reliable train number; and computing tracking intervals of a train by the aid of the early warning server, and controlling the dispatching command output unit to output control instruction according to a computation result. The train tracking interval real-time early warning system based on CTC and the early warning method form double channels with an ATP (automatic train protection) computation tracking interval method, a monitoring effect for a train tracking interval ATP control effect is realized, and integral safety of a train control system is improved.

An adaptive method and apparatus for accurate network traffic volume limit reporting including receiving a network traffic volume limit for each subscriber based on the subscriber's level of service and reporting the network traffic volume on a more frequent basis for subscribers who are approaching their network volume limit.

The embodiment of the invention relates to the technical field of network communication and provides a network traffic control method and device. The network traffic control method and device are used for solving the problem that bandwidth needed by a certain specific application program cannot be guaranteed during traffic control in a local area network in the prior art. The method comprises the steps of determining the application program corresponding to feature information of received data messages according to the first corresponding relation of the feature information of the data messages and the application program, determining the current bandwidth needed by the application program according to the bandwidth occupied by the received data messages of the application program, and distributing bandwidth for the application program according to the priority of the application program, the current needed bandwidth of the application program and the current available bandwidth. The network traffic control method and device enable the data messages of the application program of high priority to be processed in time.

Systems and methods for controlling traffic on a communications network include dynamic and flexible control plans that enable service providers to control or influence customer access to communications facilities. Communications facilities are monitored to identify network elements, such as cell sites, that are overloaded. When the traffic intensity on a particular facility exceeds a predetermined level, either active or passive control is imposed on subscribers that are in the geographic area of the facility. Active control involves blocking, delaying or disrupting subscriber calls during the critical time period. Passive control involves providing cost incentives or disincentives during low or peak periods of traffic intensity. Customers are notified of the commencement and/or termination of active or passive control preferably via SMS. When implemented with stationary cellular service, the systems and methods aid in traffic engineering and provide opportunities for customers to reduce the cost and increase the accessibility of telephone service.

A method, apparatus and computer program product for use in monitoring and controlling network behavior of Internet of Things (IoT) devices connected to a network. According to this approach, a set of network characteristics of an IoT device (e.g., as published by the device manufacturer) are assigned various risk values and then monitored over an initial time period to generate a “fingerprint” of the device's network flow. This flow is then transformed into one or more flow control rules representing “normal” or abnormal behavior of the IoT device. Preferably, the rules are instantiated into a network boundary control system (NBCS), such as an enterprise router, gateway, or the like, and then enforced, e.g., to generate alerts or others actions when the rules are triggered. The approach enables dynamic and automated threat detection and prevention based on anomalous and/or known-bad behavior.

The invention is applied to the technical field of computers, and provides a video chatting method and a video chatting system. The method comprises the following steps of acquiring the facial video data of a first client user, and identifying the facial vector data of the first client user according to the facial video data; and transmitting the facial vector data to a second client, so that the second client renders the facial vector data of the first client user to generate a virtual image model of the first client user, and displays the virtual image model of the first client user. According to the method and the system, the facial vector data is transmitted through a network in a video chatting process, so that network traffic is greatly reduced, the method and the system can be smoothly used in an ordinary mobile network, the data transmission speed is high, and the network traffic cost is greatly reduced.

The invention relates to a network equipment type identification method and a system based on a decision tree, belonging to the technical field of network security. The method comprises the steps of collecting data flow of network equipment, preprocessing the network flow, extracting HTTP data packets from the flow, vectorizing the characteristic data of text type, and utilizing the characteristicvector to carry out decision tree. C45 classification, the final identification of the type of equipment; The invention can adopt a decision tree based on network traffic tree. C45 algorithm identifythat type of network device. The method is based on the passive method to detect the type of unknown device, which will not be captured by the intrusion detection system. At the same time, the type of unknown device can be recognized, and the type of unknown device can be added to the system to improve the generalization ability.

A network flow controller to manage network traffic bandwidth is described. Embodiments of the network flow controller include a hierarchical flow creditor, a proportional flow creditor, and a rate limiter. The hierarchical flow creditor is configured to manage a plurality of hierarchical credit accounts. The hierarchical credit accounts include a shared credit account associated with a plurality of distinct network traffic flows for a shared bandwidth resource. The proportional flow creditor is configured to track an oversubscription of the shared bandwidth resource by the plurality of distinct network traffic flows. The rate limiter is coupled to the hierarchical flow creditor and the proportional flow creditor. The rate limiter is configured to limit at least one of the distinct network traffic flows based on the oversubscription tracked by the proportional flow creditor.

A Network Device (ND) may be configured to enable secure digital video streaming for HD (high definition) digital video systems over a standard network. The ND may operate in at least one of two modes, a co-processor mode and a stand-alone mode, and may provide at least three high level functions: network interface control (NIC), video streaming offload (VSO), and stand-alone video streaming (SVS). To seamlessly execute the VSO functionality, the ND may be configured to have two network stacks running synchronously on a single network interface having a single network address. The two network stacks may share the data traffic, while the Host network stack may act as a master, and configure the ND network stack to accept only specifically designated traffic, thus offloading some of the data processing to the processor configured in the ND. The ND network system may appear as an ordinary network controller to the Host, from which the user may configure the ND network driver to obtain/set the network address, configure the physical layer link speed and duplex mode, configure the multicast filter settings, and obtain and clear the network level statistics.

Managing network traffic to improve availability of network services by classifying network traffic flows using flow-level statistical information and machine learning estimation, based on a measurement of at least one of relevance and goodness of network features. Also, determining a network traffic profile representing applications associated with the classified network traffic flows, and managing network traffic using the network traffic profile. The flow-level statistical information includes packet-trace information and is available from at least one of Cisco NetFlow, NetStream or cflowd records. The classification of network flows includes tagging packet-trace flow record data based on defined packet content information. The classifying of network flows can result in the identification of a plurality of clusters based on the measurement of the relevance of the network features. Also, the classification of network traffic can use a correlation-based measure to determine the goodness of the network features.

A network flow control system utilizes flow-aware pause frames that identify a specific virtual stream to pause. Special codes may be utilized to interrupt a frame being transmitted to insert a pause frame without waiting for frame boundaries.

The present invention generally relates to detecting malicious network activity coming from network devices such as routers and firewalls. Specifically, embodiments of the present invention provide for detecting stealth malware on a network device by comparing inbound and outbound network traffic to discover packets originating from the network device and packets that violate configuration rules. When combined with a network traffic monitor server configured to monitor actual network traffic reports and to receive known network traffic reports from host computers, the system can detect stealth network traffic originating from both network devices and host computer systems.