Abstract: A method and a device for identification management and optimized forwarding in a large-scale polymorphic network, the method comprising the follow steps: S1, constructing a polymorphic backbone network; S2, modality identification management; S3, determining a modality to be forwarded; S4, configuring a flow table for a switching node; S5, receiving a packet by a balanced distributor, and preliminarily parsing the type of the packet; S6, parsing key field information in the packet, determining the switching nodes to be allocated according to the key field information, and transmitting the key field information to the corresponding switching node; S7, the switching node matching the stored flow table according to the key field information to determine a correct forwarding action.

Abstract: The present disclosure discloses a reinforcement learning agent training method, modal bandwidth resource scheduling method and apparatus. The reinforcement learning agent training method utilizes a reinforcement learning agent to continuously interact with a network environment in a polymorphic smart network to obtain the latest global network characteristics and output updated actions. By adjusting the bandwidth occupied by modals, a reward value is set to determine an optimization target for the agent, the scheduling of modals is realized, and the rational use of polymorphic smart network resources is guaranteed. The trained reinforcement learning agent is applied to the modal bandwidth resource scheduling method, and can adapt to networks with different characteristics, and thus can be used for intelligent management and control of polymorphic smart networks and has good adaptability and scheduling performance.

Abstract: A method, an apparatus and a medium for optimizing allocation of switching resources in the polymorphic network. The method selects the ASIC switching chip, FPGA and PPK software switching on the polymorphic network element based on machine learning, and specifically comprises the following steps: manually pre-configuring, formulating basic rules for polymorphic software and hardware co-processing; offline learning, designing training configuration in the offline learning stage to capture different switching resource usage variables, running experiments to generate the original data of a training classifier, and using the generated performance indices to train the model offline; and online reasoning, obtaining switching resource allocation advises, and updating modality codes according to the switching resource allocation advises.

Abstract: Provided are a method for service status analysis, a server, and a storage medium. The method includes: for each vertex of multiple vertices in a graph database: reading out attribute data of the vertex, where the graph database is generated in advance according to a service description table, the vertex represents a service, and the attribute data of the vertex includes at least one service attribute of the service represented by the vertex; and according to the attribute data of the vertex and attribute data of each of multiple related vertices, determining a service status level of the service represented by the vertex, where each of the multiple related vertices has a propagation relationship with the vertex; and according to the service status level of each of the services represented by the vertices, analyzing a service propagation network.

Abstract: The present disclosure discloses a reinforcement learning agent training method, modal bandwidth resource scheduling method and apparatus. The reinforcement learning agent training method utilizes a reinforcement learning agent to continuously interact with a network environment in a polymorphic smart network to obtain the latest global network characteristics and output updated actions. By adjusting the bandwidth occupied by modals, a reward value is set to determine an optimization target for the agent, the scheduling of modals is realized, and the rational use of polymorphic smart network resources is guaranteed. The trained reinforcement learning agent is applied to the modal bandwidth resource scheduling method, and can adapt to networks with different characteristics, and thus can be used for intelligent management and control of polymorphic smart networks and has good adaptability and scheduling performance.

Abstract: The present disclosure belongs to an identity authentication technology in network security field, and relates to a lightweight identity authentication method. The method utilizes lightweight operations of the physical unclonable function, Hash operation, XOR operation, etc.

Abstract: The present invention relates to the technical field of computer networking, in particular to an ID/location hybrid forwarding method based on source routing, including a message format based on an extension header of a MobilityFirst protocol, a source routing forwarding mechanism based on ID identifiers and a source routing forwarding mechanism based on location identifiers.