248 results about "Software define network" patented technology

The invention discloses a network security active defense method and system for detecting abnormal network behaviors. The method comprises the following steps: S1, constructing a virtualized isolation network environment through a software defined network technology; s2, deploying intelligent dynamic camouflage nodes in the virtualized isolation environment; s3, acquiring a disguised node attack behavior, and identifying a network abnormal behavior through a deep learning technology; s4, extracting network abnormal behavior features, and identifying potential threats through a deep learning technology; s5, a defense mechanism is automatically triggered, a honeynet is started, and abnormal behaviors and potential threats are isolated; and S6, tracing an attack path, and carrying out source positioning. The network security active defense system comprises a network construction module, a deployment module, an identification module, a honeynet, an isolation module and a tracing module. The disguised nodes are deployed through the deployment module to detect abnormal network behaviors, active defense of network security is realized through the honeynet, the isolation module and the tracing module, and the network security is improved.

The embodiment of the invention provides a security authentication method, device and system for a test platform, and a storage medium, and relates to the technical field of network security. The method comprises the following steps: performing fingerprint acquisition on virtual and real equipment to generate a unique identifier, dynamically slicing based on a software defined network to form an isolated test environment, and injecting a zero-trust access strategy associated with the fingerprint; and based on the zero-trust access policy and the device fingerprint, allocating a minimum permission through an attribute-based access control model, continuously evaluating device behaviors to calculate a dynamic trust score, and adaptively updating the permission according to the trust score. By constructing a full-process security certification system and through multi-dimensional mechanism cooperation, dynamic risk perception and timely strategy adjustment, fine-grained control of virtual and real equipment access in an industrial control scene is realized, the dynamic adaptability and risk resistance of a test platform in a complex network environment are improved, and the test efficiency is improved. Equipment access safety, authority distribution minimization and behavior monitoring continuity are ensured, and the safety risk of the industrial control system is reduced.

The invention relates to the technical field of power distribution station supervision, in particular to an intelligent auxiliary and artificial intelligence visual gateway control method and system for a station building, which monitors network state parameters in real time, calculates a network quality score and dynamically selects an optimal transmission path based on a reinforcement learning algorithm. Comprising a high bandwidth mode, a low delay mode and a disaster recovery backup mode, stage processing and dynamic compression are performed on transmission data according to data types and network quality scores, link switching and a local caching mechanism are automatically triggered when a network is abnormal, and finally, multi-gateway load balancing and visual monitoring are realized through a software defined network architecture. The problem of transmission delay or interruption caused by the fact that a static data transmission framework cannot adapt to network state changes in the prior art is effectively solved, and the reliability and the real-time performance of data transmission are remarkably improved through dynamic path optimization, intelligent hierarchical transmission and a fault self-healing mechanism.

The invention provides an SDN (Software Defined Network) inter-domain traffic engineering method based on reinforcement learning, which comprises the following steps of: deploying a data traffic demand monitoring platform and a control system, and constructing a global network topological graph; calculating a short link identifier for the link in the network and distributing the short link identifier to each network device; flow judgment is carried out, upward notification is carried out according to requirements, and pre-operation of intelligent routing is cooperatively completed; deploying a reinforcement learning model in the total intelligent body, outputting an optimal cross-domain path strategy to the cooperative controller, disassembling the optimal cross-domain path strategy into flow table rules which can be executed by each domain, and issuing the flow table rules to local controllers of related domains; and each local controller pushes the flow table configuration to the domain switching equipment to complete the forwarding decision of the flow. According to the method, a complete closed-loop process of flow measurement, intelligent decision making, cross-domain control and path issuing is realized, feasible reference is provided for actual deployment of an intelligent network, and the method has good engineering popularization value and is suitable for intelligent scheduling scenes such as an operator backbone network, an industrial internet and metro edge cloud.

The invention discloses a hospital internal network security isolation method based on an all-optical network architecture. The method comprises the following steps: S1, dividing a hospital internal network into a plurality of virtual optical slices by using an all-optical network slicing technology; s2, performing time domain isolation by using an optical time domain multiplexing technology, and transmitting data on different optical wavelengths by using a wavelength selective switching technology; s3, monitoring and managing bandwidth allocation through a software defined network control platform; s4, traffic prediction and resource optimization are carried out based on a decision tree integration algorithm; s5, detecting potential anomalies and security threats by using the graph neural network; s6, abnormal behaviors are detected through an isolation forest algorithm, and abnormal traffic is marked and isolated; s7, recording a network traffic log through a block chain technology; and S8, when a network fault occurs, using an intelligent self-healing mechanism to automatically switch redundant optical path resources and recover the optical path. According to the invention, all-optical network slicing and artificial intelligence technologies are fully utilized, and security isolation and resource optimization of a hospital internal network are realized.

The embodiment of the invention provides a distributed medical equipment management platform and method based on a zero-trust network. In the distributed medical equipment management platform based on the zero-trust network, an edge trusted access module adopts a hardware root of trust mechanism, so that equipment accessed for the first time is connected to a temporary supply network isolated from a medical core network, and a zero-trust security normal form is followed; the distributed trust anchor point cluster constructs a distributed identity verification network based on federated learning, and the access pre-authorization authority is verified through a device distributed identity identification (DID); the dynamic permission arrangement engine injects an attribute-based access control strategy, generates a permission token in combination with network micro-segmentation, and realizes two-way encryption communication between the equipment and a target service; the intelligent isolation gateway cuts off temporary connection through a software defined network technology and sends a network access state proof through a block chain mechanism, and edge module initialization is executed by a trusted node outside the medical cloud.

The embodiment of the invention provides a communication mode self-adaptive switching method, device and equipment based on an SDN (Software Defined Network), which are applied to the technical field of communication, and the method comprises the following steps: receiving a real-time monitoring result reported by a channel state monitoring unit, analyzing the monitoring result, and obtaining a current communication demand and channel state information; according to the current communication requirement and the channel state information, whether communication mode switching or transmission resource redistribution needs to be carried out is judged; if communication mode switching or transmission resource redistribution needs to be carried out, a corresponding instruction is generated and sent to the programmable network equipment; therefore, after the programmable network device receives the instruction, corresponding operation is executed, and communication mode switching or transmission resource redistribution is completed. In this way, the limitation of a single communication mode and fixed transmission resource allocation can be effectively overcome, and a more reliable, efficient and flexible communication solution is provided for the modern communication field.

The invention discloses a communication network bandwidth resource visualized dynamic allocation method and system based on an SDN, and relates to the technical field of communication networks, and the method comprises the steps: obtaining the channel interference intensity, the spectrum occupancy rate and the service flow priority data of each node in a wireless network based on a southbound interface of an SDN controller, and obtaining the channel interference intensity, the spectrum occupancy rate and the service flow priority data of each node through a channel resource dynamic allocation table; generating a multi-dimensional resource view; according to the multi-dimensional resource view, multi-level coverage domains of the wireless network are divided, the service load level of each coverage domain is identified, and a pre-allocation scheme of cross-domain bandwidth resources is generated; and executing a pre-allocation scheme of cross-domain bandwidth resources by using a northbound interface of the SDN controller, and adjusting bandwidth allocation parameters of each coverage domain. According to the invention, visual monitoring, accurate division and flexible scheduling of the network resource state are realized, and the utilization efficiency of network bandwidth resources is remarkably improved.

The invention relates to the technical field of computing power optical access networks, in particular to a cloud-side elastic PON resource management method, system and device based on an SDN (Software Defined Network). A flexible optical distribution network ODN of the network is formed by using an array waveguide grating, partition management and global control are performed on a cloud-edge elastic PON based on an SDN technology, partition fixed-point unloading is performed on user tasks by using a time delay-load cooperative unloading method and a routing strategy, and server computing resources and link spectrum resources are allocated. The problems that distributed computing nodes in an optical access network cannot sense each other, cooperative work cannot be achieved, computing resources are wasted due to the fact that computing tasks cannot be scheduled to an optimal node for computing, and the service quality cannot be guaranteed are solved. Under the computing power optical access network architecture, the end-to-end time delay demand of task unloading and the optimal network resource allocation are considered at the same time, the overall resource utilization rate of the network is further improved, and the average end-to-end time delay and the blocking rate are reduced.

The invention provides a collaborative data migration scheduling method based on topology awareness and deep reinforcement learning, and the method comprises the steps: initializing system configuration, constructing a weighted graph comprising a source server, a working machine, a target storage and network equipment, carrying out the embedded processing of a graph structure through a graph neural network, and generating a node and a path representation vector; in combination with source data, a working machine and a target storage state, constructing a system state vector, inputting the system state vector into a deep reinforcement learning agent for decision making, and outputting a composite action including data block selection, working machine selection, path indexing and migration rate suggestion; the network control module converts path information into flow rules, the flow rules are issued and executed through the software defined network controller, the task management module continuously monitors the migration state and processes failed data blocks, and the integrity and non-repeatability of migration tasks are ensured. According to the method, the defects of an existing data migration system in the aspects of task allocation, path selection, resource control and dynamic adaptability can be overcome.

The invention provides a low-delay SDN (Software Defined Network) flow table updating method, and belongs to the technical field of network communication. The method comprises the following steps: firstly, proposing a shortest mobile rule chain for updating a single forwarding rule; and then, aiming at a plurality of concurrency rules which simultaneously reach the switch, designing a dynamic mode to update the concurrency rules, and heuristically updating the plurality of rules in a TCAM limited area, so that the flow table updating efficiency is improved. The method and the device are used for reducing updating delay caused by the rule dependency relationship.

The invention discloses an optimization system and method for SDN traffic engineering, and the method specifically comprises the following steps: S1, obtaining a traffic matrix in a network, and representing the network topology as a graph structure; s2, inputting the traffic matrix and the graph structure into a constructed feature extraction model, and outputting a feature vector; s3, inputting the feature vector into a deep reinforcement learning model for iterative optimization, and outputting an initial flow distribution proportion strategy; and S4, performing constraint optimization on the initial flow distribution proportion strategy, and outputting a final flow distribution strategy.

The invention discloses a smart campus Internet of Things multi-service collaborative scheduling control system based on an all-optical network, and relates to the technical field of Internet of Things, and the system comprises an Internet of Things digital platform which is in communication connection with the following modules: an all-optical network infrastructure module which is used for constructing an optical fiber backbone network in a campus, and each subsystem in the campus is accessed. The reliability and stability of the campus Internet of Things are enhanced by constructing all-optical network infrastructure, the subsystems are directly connected through optical fibers by adopting the passive optical network technology, and the use of active equipment is reduced, so that the equipment failure rate and the maintenance cost are reduced, optical wavelength resources are dynamically allocated by utilizing a software defined network controller, and the reliability and the stability of the campus Internet of Things are improved. The transmission path is adjusted according to the service priority, efficient operation of key services is ensured, network stability is improved, high speed and safety of data transmission are guaranteed, and solid network support is provided for various intelligent applications in the campus.

The invention relates to the technical field of communication, and discloses a route synchronization method, device, system and equipment among multiple SDN clusters and a medium, and the method comprises the steps: obtaining neighbor configuration information issued by a central controller based on a target BGP service; establishing a neighbor relationship between the target BGP service and other BGP services according to the neighbor configuration information; obtaining first node address information of a target SDN cluster collected by the target SDN controller based on the target BGP service, and generating first routing information; and sending the first routing information to other BGP services which establish the neighbor relationship. According to the invention, the BGP service and the SDN controller are utilized to realize the coordination of the control planes, the BGP service and the SDN controller form a double-layer control plane, and the double-layer control plane is utilized to issue the routing forwarding strategy, so that flooding on the data plane is effectively avoided, and the routing synchronization efficiency is improved.

The invention provides a flow control method based on a software-defined network, which belongs to the technical field of data processing, and comprises the following steps of: 1, acquiring port data and link data of a switch in the software-defined network, and determining a network topology structure; 2, acquiring real-time flow data in the software defined network, and analyzing a network topology structure and the real-time flow data to determine adjustment path data; 3, determining a traffic adjustment strategy and a traffic forwarding rule based on the network topology structure and the adjustment path data; and step 4, executing the flow forwarding rule to realize the flow control of the software defined network. The method can deal with sudden flow fluctuation and link faults in the network and quickly respond, automatically optimize flow distribution and route selection, improve the bandwidth utilization rate and resource utilization rate of the network, reduce congestion and delay, adapt to a complex flow mode and a high-load and time-varying network environment, and improve the flexibility, reliability and efficiency of the network.

A method for deep packet inspection (DPI) in a software defined network (SDN). The method includes configuring a plurality of network nodes operable in the SDN with at least one probe instruction; receiving from a network node a first packet of a flow, the first packet matches the at least one probe instruction and includes a first sequence number; receiving from a network node a second packet of the flow, the second packet matches the at least one probe instruction and includes a second sequence number, the second packet is a response of the first packet; computing a mask value respective of at least the first and second sequence numbers indicating which bytes to be mirrored from subsequent packets belonging to the same flow; generating at least one mirror instruction based on at least the mask value; and configuring the plurality of network nodes with at least one mirror instruction.

A distributed network that may include multiple links; and multiple physical nodes that in communication with each other via the multiple links, to form multiple redundant paths, wherein at least some of the redundant paths form a daisy chain; wherein the multiple physical nodes comprise output ports configured to interface with external hosts. The multiple physical nodes are configured to supports software defined networking (SDN).

The invention discloses an edge data transmission optimization method based on energy consumption minimization and load balancing, which belongs to the technical field of edge computing, is realized through a system consisting of a central control server, an edge node, a transmission link and an SDN (Software Defined Network) controller, and comprises the following steps: S1, network topology modeling and data collection; s2, link selection optimization is carried out; s3, topological optimization and dynamic adjustment are carried out; s4, executing and controlling data transmission; s5, performing adaptive dynamic adjustment; and S6, the steps S1 to S5 are executed in a circulating mode. According to the method, a multi-target reinforcement learning algorithm is adopted, and joint optimization of energy consumption minimization and load balancing is achieved in edge data transmission; transmission links are dynamically adjusted and redundant nodes and links are closed, so that the overall energy consumption of the system is reduced; through link classification and load balancing optimization, the data transmission efficiency and the network stability are improved; and a heuristic factor and a Chebyshev scaling function are introduced, so that the convergence speed of the algorithm is accelerated.

The present disclosure relates generally to an SDN providing DNS resolution for a non-SDN service. In particular, the SDN hosts a first endpoint communicatively coupled with the non-SDN service. The SDN also hosts a DNS proxy in support of a second private network of a customer. A second endpoint of the customer can be accessible via the second private network The non-SDN service can send a DNS resolution request (that includes, for example, an FQDN of the second endpoint) to the first endpoint. The first endpoint forwards this request to the DNS proxy. In turn, the DNS proxy determines the IP address of the second endpoint and returns a reserved IP address corresponding to the DNS proxy and mapped to the second endpoint's IP address. The first endpoint sends this DNS resolution to the non-SDN service that then learns that the FQDN is resolved to the reserved IP address.

The invention relates to the technical field of computer network optimization and controller load balancing, and discloses an SDN (Software Defined Network) controller load balancing method based on a graph neural network and an evolutionary algorithm. Constructing a graph model and splicing to obtain a feature matrix; normalizing an adjacent matrix, performing feature aggregation and updating on a feature matrix by using a multilayer graph convolutional network, outputting a high-dimensional embedded vector, setting a final state representation and reward function of a controller, and obtaining a scheduling strategy adapted to a dynamic network environment through meta-reinforcement learning and actor reviewer network training; performing global search on parameters of meta reinforcement learning in a simulation environment to find an optimal solution; the method comprises the following steps: acquiring the newest state of each controller in real time, updating and embedding through a pre-trained GCN, outputting a scheduling strategy by using a trained meta-reinforcement learning model according to a global state, and performing online parameter fine tuning and updating on a network by using Q-learning at the same time.

The invention relates to an SDN-based subnet address allocation method and system. The method comprises the steps of obtaining an initial network configuration requirement; collecting network information in real time; based on network information and initial network configuration requirements, dynamically dividing address fields by adopting a self-adaptive subnet division algorithm, and generating corresponding subnet configuration and a dynamic address pool; when a DHCP request is received, an available IP address is selected from the dynamic address pool according to a pre-formulated IP address allocation strategy, a corresponding DHCP response message is generated, and the DHCP response message is sent to request equipment to complete address allocation; real-time data after address allocation are monitored in real time, the real-time data comprise IP address use conditions, equipment load data and topological changes, and subnet configuration and a dynamic address pool are dynamically adjusted according to the real-time data. The application has the effect of improving the utilization efficiency of the address resources.

The invention relates to the technical field of network security, and discloses an SDN (Software Defined Network)-based large-scale host vulnerability identification and attack response simulation method, which comprises the following steps of: 1, creating a network topology through a Mininet script; the method comprises the following steps of: 1, acquiring a host vulnerability identifier, 2, configuring vulnerability attributes for the host by using a JSON format in an SDN network environment, and 3, based on the host vulnerability identifier, selecting a corresponding attack strategy and executing a simulation attack, monitoring network flow and a host state in real time through an SDN controller, identifying an abnormal behavior and triggering a response mechanism. According to the method, the vulnerability attribute in the JSON format is configured for each host node, and an attacker is supported to detect and position the target host based on specific vulnerability characteristics by utilizing the centralized analysis capability of the SDN controller, so that the simulation capability of a network security simulation environment to a real attack scene is remarkably improved; the limitation that a traditional method is difficult to reflect vulnerability details is solved.

The invention discloses a virtuality and reality combined method and system for dynamically reconstructing diversified network topology, and belongs to the technical field of novel power systems, and the method comprises the following steps: virtualizing physical equipment in a power system business scene network by using NFV to obtain virtual equipment; constructing a network architecture for realizing a service scene network of the power system by adopting a virtualization mode; physical equipment and virtual equipment in the service scene network of the power system are controlled in a centralized manner based on the SDN; and the SDN controller dynamically reconstructs the service scene network of the power system according to a set strategy. According to the invention, dynamic allocation of network resources and real-time reconstruction of topology are realized through a virtuality and reality combination technology, a software defined network technology and a network function virtualization technology, and virtualized resources and resources of physical equipment can be effectively integrated so as to quickly respond to changes of application requirements.

The invention discloses a flow control method and device based on an SDN, equipment and a medium, and belongs to the technical field of communication. The method comprises the following steps: optimizing a strategy-evaluation network based on real-time network state information of the SDN to obtain an original traffic strategy of a tunnel output by the optimized strategy-evaluation network, the original traffic strategy comprising a demand traffic corresponding to the tunnel of the SDN and an original traffic distribution proportion of each path in the tunnel, the strategy-evaluation network is used for maximizing accumulated rewards and ensuring random exploratory performance of a flow strategy, and reinforcement learning is carried out on the flow strategy with better QoS in a complex network state environment; under the condition that the MLU corresponding to the original flow strategy exceeds a preset safety threshold value, searching a flow distribution proportion meeting an MLU safety condition in a local flow distribution proportion area containing the original flow distribution proportion as a safety flow distribution proportion; and controlling the traffic distribution of each path in the tunnel in the SDN according to the safe traffic distribution proportion. According to the embodiment of the invention, the stability of the SDN can be improved.

Techniques are disclosed for processing data packets and implementing policies in a software defined network (SDN) of a virtual computing environment. At least one network device is configured to disaggregate enforcement of policies of the SDN from hosts of the virtual computing environment. Tier-0 devices are communicatively coupled to network interfaces of the network device. The network device comprises a plurality of data processing units that are configured to implement functionality of the network device.

The invention discloses a gray scale test method of a cloud mobile phone server and related equipment, and relates to the technical field of cloud computing, and the method comprises the steps: obtaining a file system of an old version server and a file system of a new version server; based on the file system of the old-version server and the file system of the new-version server, performing difference analysis, and generating an incremental snapshot file; selecting a part of nodes in the server cluster to load the incremental snapshot file, and constructing a gray scale test environment; distributing the user request to a gray level test environment and a stable environment according to a preset distribution proportion through the software defined network; and collecting performance indexes of the gray test environment in real time, triggering a rollback mechanism when detecting that the performance indexes of the gray test environment exceed a preset threshold value, unloading the incremental snapshot file, and switching the corresponding node to an old version.

The invention discloses a hybrid SDN (Software Defined Network) link fault protection method based on key network elements, and belongs to the technical field of Internet technologies. According to the method, the LFC rule is used for remarkably reducing the number of target nodes affected when the link fails, conventional candidate SDN node judgment is optimized, the number of the candidate SDN nodes is increased, and finally, the minimum number of SDN switch nodes are flexibly selected according to key network elements. According to the scheme provided by the invention, the problem of most single link faults in the network can be solved, the path stretching degree is relatively small, the deployment cost is relatively low, and the network oscillation and delay are relatively low. Therefore, the invention provides an effective solution for improving the protection efficiency of the hybrid SDN link fault.

The invention provides a network equipment management method based on a software defined network, which comprises the following steps: constructing a switch network comprising an airborne network centralized control management end and airborne equipment, the airborne equipment comprising a master switch, a slave switch and an airborne node computer, the main switch is an airborne device directly connected with the airborne network centralized control management end; a static flow table is configured, the static flow table is stored in all airborne equipment, and the static flow table is used for specifying an MAC address of an airborne network centralized control management end and a cascade port corresponding to each airborne equipment; during power-on, connection between the airborne network centralized control management end and the airborne equipment is established; and at the airborne equipment end, periodically triggering or event triggering network topology discovery, identifying new airborne equipment in the switch network by utilizing the network topology discovery, and checking the operation state of the airborne equipment. By dynamically identifying the airborne equipment, the network topology dynamic management and the plug-and-play function of the airborne equipment can be effectively supported.

The invention discloses an advanced network threat intelligent detection and defense system and method in the technical field of network defense. The system comprises a data acquisition module which analyzes encrypted traffic through a deep packet inspection technology and marks space-time metadata; the feature extraction module is used for constructing a node behavior association graph by adopting a multi-scale sliding window and a graph neural network; the detection engine module integrates multi-model parallel analysis of LSTM, random forest and the like, and dynamically fuses detection results through an entropy weight method; and the defense execution module is used for realizing flow cleaning and virtual trapping node deployment based on an SDN (Software Defined Network) technology. The method comprises the steps of protocol extension field reverse analysis, covert channel time sequence correlation analysis and dynamic network topology adjustment. According to the method, the encryption APT attack detection accuracy reaches 98.7%, the false alarm rate is reduced to 0.15%, the defense strategy effective time is shorter than 1 second, and the method is suitable for complex network environments such as cloud computing and industrial Internet of Things.

An enhanced router is described that improves network performance for AI workloads by providing in-network primitives that improve the performance of operations such as Broadcast and Reduce operations. The enhanced router leverages the observation that operations such as the Broadcast and Reduce primitives are more performant (e.g., reduced latency and bandwidth) when performed in the network rather than in graphics processing unites (GPUs) or central processing units (CPUs) which are often deployed as leaf nodes in a typical network topology in a datacenter.