186 results about "Network segmentation" patented technology

The invention discloses a risk management system and method based on multi-source data perception, and relates to the technical field of cloud computing security, and the method comprises the steps: collecting historical multi-source data of a cloud host; constructing a cloud host risk portrait based on historical multi-source data of the cloud host; the method comprises the following steps: dividing a cloud host network into a plurality of security domains according to service functions and security requirements, and configuring a basic access control strategy; sensing the resource use condition of the cloud host in real time, and identifying the current service state of the cloud host in combination with the service log; and matching a preset security policy according to the current service state of the cloud host and the risk portrait of the cloud host, and dynamically adjusting a network segmentation policy and an access control policy in a security domain based on a micro-isolation technology. According to the method, a micro-isolation technology is used, a network segmentation strategy and an access control strategy in a security domain are dynamically adjusted according to the real-time service state and the risk portrait of the cloud host, and the security and the stability of the cloud host in different service scenes are improved.

The invention relates to the field of image segmentation, and discloses a liver and tumor segmentation method based on mixed attention, and the method is realized by adopting a liver and tumor segmentation network based on mixed attention, and comprises two branches: a local feature extraction branch proposes a local deep splitting module to extract local information, extracts context information in a multi-scale manner, and extracts a local feature; the method has the advantages that the influence of useless noise is inhibited, fine-grained feature representation is integrated, the segmentation precision and remote information dependence are improved, CNNform remote dependence branches are constructed based on a Transform model, global context information is concerned, global features are captured, category information is refined, the parameter quantity is reduced, and the segmentation efficiency is improved. An interactive fusion module is proposed based on cross attention to aggregate feature mappings obtained from two branches to further improve network segmentation performance, and finally, a deep loss supervision mechanism is integrated between different levels of a network decoder to provide additional feedback from a deeper intermediate layer, which restricts network weight in a target area to improve network segmentation performance. And optimizing the segmentation result.

The invention relates to the technical field of image target detection, and discloses a hyperspectral image segmentation method based on spectrum-spatial feature combination, comprising the following steps: 1) acquiring a hyperspectral image data set; 2) performing hyperspectral image segmentation based on multi-scale space-spectral feature combination of deep learning; and 3) evaluating a network segmentation effect. The hyperspectral image is segmented by using a deep learning method, the spectral features and the spatial features of the hyperspectral image are effectively extracted and fused by providing a spectral-spatial feature combined segmentation method, and meanwhile, the prediction accuracy of the model is improved.

Implementation(s) for multi-factor network segmentation are described. A plurality of packets at a higher layer of a network stack is processed, where at least one packet of the plurality of packets was previously determined, as part of processing the at least one packet at lower layers of the network stack, to be authorized to be processed by the higher layer. Specifically, responsive to successful authentication of a cryptographic certificate received during the handshake process, a second service is identified from the cryptographic certificate. It is determined, based on a security policy, that the second service is authorized to access the first service. Responsive to the determination, a configuration is caused such that packets sent using the source address are now authorized to be processed by the higher layer.

Current OT security solutions mostly provide defensive security involving monitoring and network segmentation. However, offensive security, proven effective in IT systems, is not easily applicable to OT systems due to its sensitive nature and high availability requirements. To conduct offensive security measures in OT networks, the concept of Digital Twins emerges as a viable solution but with limited progress in developing virtual replicas that accurately simulate real environments. Further, the process of creating such replicas is inherently complex and time-consuming, often involving extensive manual work. The present invention discloses an environment named as “Battle Twin,” a specialized playground designed for breach and attack simulations specifically designed for OT networks. Battle Twin focuses on mimicking the security posture of the underlying network rather than replicating its production or behavioral aspects. By leveraging Battle Twin, security experts may effectively test and evaluate offensive security strategies without even touching real OT environments.

The invention discloses a bladder tumor image segmentation method and system based on FEBE-Net, which are applied to the technical field of image processing, and adopt a PVTv2 encoder to learn multi-scale feature representation so as to adapt to the change of the size and shape of a bladder tumor; secondly, a feature exploration attention module is provided to fully mine potential local detail information in shallow features extracted by a PVTv2 encoder, noise is eliminated, missing fine-grained details are supplemented for a decoding stage, and the network segmentation performance is improved; meanwhile, a boundary enhancement and refinement module (BER) is provided, the boundary of the bladder tumor is additionally learned through a boundary detection operator to enhance a boundary region in input features of a decoder end, and a final prediction feature map is refined and adjusted; an efficient self-attention calibration decoder module is provided, and global context information and local detail information are gradually and effectively recovered from high-level features and low-level features with the help of boundary clues provided by a BER module.

In response to a programmatic request, configuration information representing a multi-network-segment gateway established on behalf of a customer is stored at a networking service. In response to another programmatic request, a communication session is established between a route signaling node of the gateway and a routing information source located at a customer premise. In response to additional programmatic input, the networking service stores an indication that the gateway is to be used to transfer packets between a cloud-side virtual network and a customer-side virtual network. The routing information exchanged in the session pertains to the cloud-side and customer-side virtual network, and is used to transfer data packets between the two virtual networks.

A method for dynamically managing network segmentation, slicing, and scaling operations in an edge computing system includes retrieving system parameters and device credentials from a secure database using a processing system. The method involves segmenting the network by analyzing device types, capabilities, and roles, categorizing devices into logical groups, assigning devices to these groups, selecting appropriate policies, and storing group membership and policy information in a central database. It configures network slices by determining the need for dedicated slices for new service requests, initiating network discovery to map topology, selecting and assigning network elements, configuring and activating the slice, updating routing tables, enforcing access controls, and dynamically allocating resources in real-time based on each slice's requirements.

In general, techniques are described for performing network segmentation for container orchestration platforms. A network controller comprising a memory and processing circuitry may be configured to perform the techniques. The memory may be configured to store a request, conforming to a container orchestration platform, to configure a new pod of a plurality of pods with a primary interface to communicate on a virtual network to segment a network formed by the plurality of pods. The processing circuitry may be configured to configure, responsive to the request, the new pod with the primary interface to enable communications via the virtual network.

The invention belongs to the technical field of intelligent detection of new energy equipment, and particularly relates to a solar panel defect detection method and system, computer equipment and a storage medium, and the method comprises the following steps: a lightweight defect preliminary screening and adaptive shooting step: carrying out the recognition and image collection of a solar panel array through a lightweight model at the edge end of an unmanned aerial vehicle; a defect segmentation and type identification step: performing accurate segmentation on the solar panel and the defects through a neural network model, and judging the types and grades of the defects in combination with a feature extraction and classification model; a defect enhancement optimization step: enhancing the defects through an adversarial network; a detection performance evaluation step: quantitatively evaluating the overall performance of the system through a multi-dimensional index; mSAN-Net network segmentation is adopted, so that the defect detection precision is improved; and in combination with a GAN defect enhancement technology, the omission ratio and the false detection rate of weak defects are reduced, so that the fine operation and maintenance requirements of the solar panel are met.

The invention discloses an underwater foresight sonar image segmentation method, system and device based on CNN and Transform, and relates to the technical field of digital image processing and mode recognition. The method comprises the following steps: S1, collecting image data collected by a foresight sonar, carrying out corresponding labeling, and obtaining a training set and a verification set after data enhancement through data processing; s2, performing feature extraction on the image through a cascade convolution module and a hybrid Transform model, wherein the convolution module pays attention to local semantic information of the image; in a data processing stage, data enhancement is realized by fusing technologies such as Contourlet transformation, in a network architecture, an encoder combines convolution and hybrid Transformer, learning of local and spatial information of an image is considered, deep feature fusion and data value mining are carried out by utilizing Swin Transformer and the like, and particularly, it is worth mentioning that an edge feature module can learn edge information of a target area, and the edge feature module can learn the edge information of the target area; the segmentation result is explicitly corrected, the network segmentation performance is comprehensively improved, and the accuracy of sonar image analysis is powerfully guaranteed.

Embodiments of the disclosure provide distributed authentication with network segmentation and dynamic authorization for networks. The system may include a device within a network of devices. An identifier is within the device and includes a cryptographic certificate. The device is configured to transmit the identifier to an authenticator as a security proof. The authenticator is configured to disable the device from performing at least one operation within the network before verifying an identity of the device via the identifier.

The invention provides a similarity memory prior network segmentation method and system for medical image segmentation, and particularly relates to the technical field of image processing. A similarity memory prior network segmentation method for medical image segmentation comprises the following steps: (1) completing feature extraction through a first-stage encoder and a second-stage encoder, and extracting key features from a medical image; (2) fusing stage characteristics corresponding to the first-stage encoder and the second-stage encoder through a network, and transmitting the stage characteristics to a jump connection and decoder; (3) in jump connection, channel attention and space attention are used to fuse corresponding characteristics of a first-stage encoder and a second-stage encoder; and (4) decoding: using DMW-LA to extract deep semantic information. The method has the beneficial effects that key information in a medical image can be efficiently captured and utilized, so that higher accuracy and robustness are achieved in a segmentation task, the global relevance and internal consistency among features are improved, and the representation capability of a network is enhanced.

A system for zero-trust cybersecurity access control using continuous identity verification (100), comprising: a Policy Decision and Enforcement Orchestrator (1) configured to receive an access request from a user device and enforce a session with minimal privileges; an engine for continuous identity verification (2) configured to generate a time-dependent identity trust score by continuously verifying the user identity during an active session; a device state and telemetry collector (3) configured to acquire device state parameters, runtime signals and network telemetry and to generate a state value; a module for detecting behavioral risks and anomalies (4) that is configured to create behavioral profiles and detect anomalies based on user activity patterns, contextual signals and the history of resource access; a cryptographic authentication and secure token module (5) configured to issue and update a short-lived, bound access token associated with at least the identity trust value and the state value; and an adaptive response and microsegmentation module (6) configured to dynamically adjust access permissions, network segmentation and session privileges in real time, based on an aggregated risk assessment derived from modules (2) to (4), where the system (100) continuously reassesses trustworthiness during the session and selectively allows, restricts, requires enhanced authentication or terminates the session based on the aggregated risk assessment.

The invention relates to the technical field of image processing, and discloses a three-dimensional medical image segmentation method, which comprises the following steps: acquiring two prediction segmentation probability graphs of each sample based on two parallel sub-networks of a dual-network segmentation model, and calculating a soft pseudo label of an unlabeled sample; utilizing a distance regression head and a hyperbolic tangent function to obtain two prediction symbol distance fields of each sample based on the decoding feature map; calculating segmentation loss and regression loss of each labeled sample, and adding the segmentation loss and the regression loss to obtain supervision loss; calculating the pseudo label consistency loss and the symbol distance consistency loss of each unlabeled sample, and adding the pseudo label consistency loss and the symbol distance consistency loss to obtain consistency loss; optimizing the dual-network segmentation model under the joint constraint of supervision loss and consistency loss; and obtaining an input segmentation result of the to-be-segmented three-dimensional medical image by using the trained dual-network segmentation model. According to the method, the problem of class imbalance in the three-dimensional medical image is effectively relieved, and the segmentation precision of the weak edge region is improved.

A lung CT blood vessel image segmentation and reconstruction system based on multiple interpretable features performs further fine-grained segmentation on the basis of main airway features through a feature extraction module to obtain a plurality of connected domains, performs multi-stage feature extraction on the connected domains, and outputs multi-scale feature information; the blood vessel segmentation module generates an information matrix according to the indexes and the pulmonary parenchyma mask, generates a blood vessel network segmentation result through an adversarial network and performs post-processing; and the model reconstruction module performs three-dimensional reconstruction on the blood vessel network segmentation result and the pulmonary parenchyma mask to obtain a 3D model. According to the method, on the basis of anatomical priori knowledge of pulmonary vessels and airways, a large amount of multi-scale feature information is utilized, the information loss of the two-stage segmentation network is reduced, and the network adaptability and the learning ability of the relation between pixels under the complex condition are improved. In the aspect of system design, a modular design and management mode is adopted, efficient resource access is achieved, and meanwhile the operability and usability of the system are improved through end-to-end process design.

A wireless communication system includes an external provider subsystem and an electronic network subsystem in operable communication with the external provider subsystem. The electronic network subsystem is configured to provide a first microservice and a second microservice different from the first microservice. The wireless communication system further includes an in-home subsystem (i) separate from the external provider subsystem, (ii) in operable communication with the electronic network subsystem, and (iii) including a first micronet and a second micronet different from the first micronet. The first micronet is configured to operably interact with the first microservice, and the second micronet is configured to operably interact with the second microservice. The wireless communication system further includes at least one electronic device configured to operably connect with one of the first micronet and the second micronet.

As the IP network topology often has a distinct hierarchical structure, the topology can be decomposed into a plurality of areas with high cohesion and low coupling by using a community detection algorithm in a graph theory, and routing update in the areas is selectively diffused to other areas, so that the calculation amount of full-quantity routing simulation can be effectively avoided, and rapid and accurate IP network routing simulation reasoning is realized. The invention provides an IP (Internet Protocol) network routing simulation reasoning method based on community detection, which comprises the following steps of: firstly, dividing the whole network into a plurality of cluster structures based on a community detection algorithm, enabling a shortest path between nodes in a cluster not to pass through other clusters, and taking a division result as a basis for calculating routing update; then, the change operation of the network topology is accurately recognized, network element node pairs needing to be recalculated are further arranged according to network elements involved in change points and cluster information to which the network elements belong, and recalculation and routing updating are conducted on SPT trees of the node pairs. Meanwhile, the invention provides an IP network routing simulation reasoning device based on community detection, and the IP network routing simulation reasoning device comprises a topology management module, a network configuration module, a network segmentation module, a change analysis module and a routing calculation module so as to realize the topology segmentation and routing simulation reasoning process of the IP network.

The invention provides a large-scale road network parallel microcosmic simulation method, device, equipment and medium, and relates to the technical field of traffic simulation, and the method comprises the steps: carrying out the path flow distribution processing according to road network data and user heterogeneous data, carrying out the iterative calculation through combining with the sensitivity of different users to the average travel time, the travel time standard deviation and the pass cost, and obtaining a path flow distribution result; obtaining a path flow distribution scheme; matrix construction processing is carried out according to the path flow distribution scheme, and a Laplacian matrix is constructed by analyzing connectivity and structural characteristics of a traffic network; road network segmentation processing is carried out according to the eigenvalue and the eigenvector of the Laplacian matrix, and a road network segmentation result is obtained; and performing spatial decomposition processing on the road network segmentation result to obtain a spatial decomposition result, and defining vehicle driving information in a boundary region in the spatial decomposition result to obtain a simulation model. According to the invention, the problem that traffic decision makers cannot be assisted to make scientific decisions in time due to the fact that accurate prediction results cannot be fed back in time in the prior art is solved.

A wireless communication system includes an external provider subsystem and an electronic network subsystem in operable communication with the external provider subsystem. The electronic network subsystem is configured to provide a first microservice and a second microservice different from the first microservice. The wireless communication system further includes an in-home subsystem (i) separate from the external provider subsystem, (ii) in operable communication with the electronic network subsystem, and (iii) including a first micronet and a second micronet different from the first micronet. The first micronet is configured to operably interact with the first microservice, and the second micronet is configured to operably interact with the second microservice. The wireless communication system further includes at least one electronic device configured to operably connect with one of the first micronet and the second micronet.

A wireless communication system includes an external provider subsystem and an electronic network subsystem in operable communication with the external provider subsystem. The electronic network subsystem is configured to provide a first microservice and a second microservice different from the first microservice. The wireless communication system further includes an in-home subsystem (i) separate from the external provider subsystem, (ii) in operable communication with the electronic network subsystem, and (iii) including a first micronet and a second micronet different from the first micronet. The first micronet is configured to operably interact with the first microservice, and the second micronet is configured to operably interact with the second microservice. The wireless communication system further includes at least one electronic device configured to operably connect with one of the first micronet and the second micronet.

A wireless communication system includes an external provider subsystem and an electronic network subsystem in operable communication with the external provider subsystem. The electronic network subsystem is configured to provide a first microservice and a second microservice different from the first microservice. The wireless communication system further includes an in-home subsystem (i) separate from the external provider subsystem, (ii) in operable communication with the electronic network subsystem, and (iii) including a first micronet and a second micronet different from the first micronet. The first micronet is configured to operably interact with the first microservice, and the second micronet is configured to operably interact with the second microservice. The wireless communication system further includes at least one electronic device configured to operably connect with one of the first micronet and the second micronet.

According to some embodiments, a method of controlling access to network resources includes: receiving an authentication request from a user device to a core security service; if the user is authenticated, authorizing the user device to connect to a private cloud, and connecting the user device with the private cloud and retrieving user-specific segmented firewall rules stored in the private cloud; routing, through the firewall rules, a request by the user device to access an outer resource; evaluating the request against the firewall rules; if the request meets the firewall rules, routing the request through security measures of the firewall; and if the request does not meet the firewall rules, denying the user device access to the outer resource.

The invention belongs to the technical field of algorithm models, and particularly relates to an image recognition model deployment method and system. In order to overcome the defect that an existing model deployment method cannot or cannot well deploy a model to different chips, the invention adopts the following technical scheme: the image recognition model deployment method comprises the following steps: converting a model to be deployed into a standard model in a uniform format; loading the standard model in a to-be-deployed chip environment, analyzing and restoring to obtain all information of the to-be-deployed model; judging whether the to-be-deployed chip supports network segmentation or not; after converting operators supported by the to-be-deployed chip, compiling the operators by adopting a compiling tool of a to-be-deployed chip manufacturer to obtain a hard computing result, and computing the operators which are not supported by the to-be-deployed chip by adopting a processor; and inputting the hard computing result into a reasoning tool of a to-be-deployed chip manufacturer, and inputting to-be-reasoned data into the reasoning tool or a processor for reasoning to obtain a reasoning result. The method has the beneficial effect that the deployment of the same algorithm model on different chips is realized.

The invention provides a network segmentation method and device, electronic equipment and a storage medium. The network segmentation method comprises the following steps: acquiring a target value of a multi-dimensional characteristic parameter of each network node in a network; clustering the network nodes based on the target values of the multi-dimensional characteristic parameters, and taking each cluster obtained by clustering as a network segment; and selecting a central node of the network segment from the network nodes contained in the network segment as a control node of the network segment. In the embodiment of the invention, each network segment can be independently controlled, the network segments do not influence each other, and when a certain node fails, only the network segment where the node is located needs to be processed without influencing other network segments, so that the maintenance of a large-scale network is converted into the maintenance of a small-scale network segment, and the maintenance efficiency is improved. The complexity of network management is reduced, and the flexibility of the network is improved.

The invention discloses a bridge structure modeling method based on three-dimensional point cloud processing, and belongs to the technical field of point cloud modeling. The method comprises the following steps: firstly, obtaining a complete surface image of a bridge, segmenting a bridge part through a segmentation network to make a mask, reconstructing a high-precision point cloud of the bridge by using a motion recovery structure method and a multi-view stereoscopic vision method, and then establishing a BIM model according to the point cloud; the problem of bridge point cloud data missing caused by the shielding problem of a ground three-dimensional laser scanner is solved, and the problems that the precision of bridge point cloud data obtained by an airborne laser radar is poor, and a bridge bottom cannot be collected are solved. A point cloud model is established by using a bridge mask through a network segmentation method, an environment point cloud does not need to be cut manually in a later period, and fine modeling is performed by generating a high-precision point cloud model and importing the high-precision point cloud model into modeling software; in addition, the method can also be migrated to point cloud modeling of other buildings and structures.

The invention discloses an automatic identification method for network security check of a power system, which relates to the technical field of automatic identification and comprises the following steps of: obtaining a plurality of groups of network segmentation granularities according to a network architecture and segmentation granularity adjustment parameters of the power system; acquiring actual application demand data of the plurality of groups of network segment granularities and security check effect data under the plurality of groups of network segment granularities; according to the actual application demand data and the security check effect data, a segmentation granularity evaluation model is constructed based on a convolutional neural network, and a segmentation granularity evaluation coefficient is generated; constructing a two-dimensional relation curve by using the segmentation granularity evaluation coefficient and the corresponding network segmentation granularity, carrying out data analysis on the two-dimensional relation curve, judging the network segmentation granularity which is most matched with a preset checking mode, and applying the network segmentation granularity to the network security checking of the power system; the method and the device are used for solving the problem that the network segmentation granularity is set based on experience, so that the effectiveness of the isolation attack and the influence on the network efficiency are unbalanced.

The invention provides a space detection positioning method and device, electronic equipment and a readable storage medium, and the method is applied to a compound eye detector, and comprises the steps: controlling the compound eye detector to capture a scene image according to a preset detection parameter; preset image processing is carried out on the scene image to obtain a target image, and the preset image processing comprises denoising processing, contrast adjustment processing, superposition processing and local pixel adjustment processing; extracting image feature information in the target image based on a network segmentation method; performing deep learning on the extraction process of the image feature information by adopting a preset deep learning algorithm; and analyzing the image feature information, and determining parameter information of the detection object and position information of the detection object in the space. According to the invention, multiple preprocessing is carried out on the scene image collected by the compound eye detector, the quality of the image collected by the compound eye detector is effectively improved, and the detection precision of the compound eye detector is further improved.

The present invention discloses a method and device for three-dimensional point cloud semantic segmentation with multi-domain attention enhancement, and relates to the field of computer vision technology. It includes: obtaining three-dimensional point cloud data to be segmented; inputting the three-dimensional point cloud data into a trained three-dimensional point cloud semantic segmentation model with multi-domain attention enhancement; wherein the three-dimensional point cloud semantic segmentation model includes a convolutional attention encoder module, a Transformer encoder module, a convolutional attention decoder module, and a segmentation head module; according to the three-dimensional point cloud data and the three-dimensional point cloud semantic segmentation model, a three-dimensional point cloud semantic segmentation result is obtained. The present invention designs a point cloud semantic segmentation method, which uses multi-domain attention to enhance the network segmentation performance while controlling the computational cost, and forms a custom configuration framework to facilitate users to control model training and other processes, and quickly deploy.

Methods and systems relate to packet forwarding that includes a network device receiving a message from a second network device and that has a destination address of a third network device. The circuitry of the network device determines that a connection between the second network device and the third network device has been previously opened by the third network device. In response to the determination that the connection is open and in response to receiving the message, the circuitry transmits the message towards the third network device.