公开(公告)号：US20230020947A1

公开(公告)日：2023-01-19

申请号：US17876553

申请日：2022-07-29

Applicant: Zhejiang Lab

Inventor： Hanguang LUO ,  Tao ZOU ,  Shunbin LI ,  Qi XU ,  Huifeng ZHANG

IPC: H04L9/32 ,  H04L9/08 ,  H04L9/06

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. for bidirectional authentication between an authentication server and an Internet of Things resource-limited device, and particularly utilizes uniqueness of an integrated circuit (IC) physical microstructure created by the physical unclonable function in the resource-limited device in a manufacturing process to design an engineering-implementable information desynchronization recovery mechanism of two authentication parties by optimizing an interaction mode of input challenge and output response of the physical unclonable function, thereby solving the problem that the same lightweight identity authentication type solution cannot ensure forward security and resist desynchronization attack, further reducing resource cost for an identity authentication process, and effectively improving security and operation efficiency of identity authentication of the Internet of Things resource-limited device.

公开(公告)号：US20240171509A1

公开(公告)日：2024-05-23

申请号：US18542823

申请日：2023-12-18

Applicant: ZHEJIANG LAB

Inventor： Congqi SHEN ,  Huifeng ZHANG ,  Tao ZOU ,  Ruyun ZHANG

IPC: H04L45/00 ,  H04L45/74 ,  H04L69/22

CPC classification number: H04L45/38 ,  H04L45/74 ,  H04L69/22

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.

公开(公告)号：US20240015079A1

公开(公告)日：2024-01-11

申请号：US18359862

申请日：2023-07-26

Applicant: ZHEJIANG LAB

Inventor： Congqi SHEN ,  Huifeng ZHANG ,  Shaofeng YAO ,  Qi XU ,  Ruyun ZHANG

IPC: H04L41/16 ,  H04L41/40 ,  G06N20/00

CPC classification number: H04L41/16 ,  H04L41/40 ,  G06N20/00

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.

公开(公告)号：US20250016070A1

公开(公告)日：2025-01-09

申请号：US18528054

申请日：2023-12-04

Applicant: ZHEJIANG LAB

Inventor： Hanguang LUO ,  Huifeng ZHANG ,  Geyang XIAO ,  Qi XU ,  Wanxin GAO ,  Jun ZHU ,  Tao ZOU ,  Ruyun ZHANG

IPC: H04L41/5019 ,  H04L41/5006

Abstract: A polymorphic network system and a polymorphic network operation method are provided. From top to bottom, the polymorphic network system sequentially includes: an application layer configured to provide network applications corresponding to network service requirements; a service layer configured to determine network capability requirements for implementing the network applications; a mode layer configured to provide corresponding network modes based on the network capability requirements; and an environment layer configured to provide network infrastructure that is capable of supporting operation of the network modes. The network infrastructure is configured to load and transmit messages corresponding to the network applications. The messages are capable of being generated, encapsulated, decapsulated, and routed and forwarded based on the network modes corresponding to the network applications.

公开(公告)号：US20240143403A1

公开(公告)日：2024-05-02

申请号：US18354601

申请日：2023-07-18

Applicant: ZHEJIANG LAB

Inventor： Huifeng ZHANG ,  Congqi SHEN ,  Tao ZOU ,  Jun ZHU ,  Ruyun ZHANG ,  Qi XU ,  Hanguang LUO ,  Xingchang GUO

IPC: G06F9/50 ,  G06N20/00

CPC classification number: G06F9/5038 ,  G06N20/00

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.