5080 results about "Networked system" patented technology

Advanced cybersecurity systems and methods may provide enhanced security of critical infrastructure without the risks associated with traditional penetration testing. The systems and methods may model network vulnerabilities and simulate attack scenarios to identify potential security breaches. This supports the integration of generic rules and common properties, allowing for dynamic and scalable vulnerability analysis across complex network systems. Additionally, the systems and methods incorporate a multi-purpose fuzzer and an AI-driven Cyber Reasoning System (CRS) that autonomously detects and remedies software vulnerabilities based on predefined logic and pattern recognition. This comprehensive approach identifies existing vulnerabilities and predicts potential future threats by analyzing changes in network behavior and data flow. The systems and methods are particularly suited for mission-critical environments where operational continuity is paramount, providing a robust tool for improved cybersecurity management without disrupting system operations.

The present invention discloses a disaster prevention, early warning and production decision support method and system for a power distribution network, and relates to the technical field of the power distribution network. Various data is fused, a maximum wind load, a lightning trip-out rate, a maximum carrying capacity at a highest operation temperature and an average failure frequency and time under an environmental factor are calculated, and a power outage risk is evaluated and classified; and after an emergency, a power grid topology is analyzed, and a device outage probability is calculated to obtain a load power outage probability and risk and provide disaster early warning, monitoring and rush repair decision support.

The invention provides a network security penetration detection method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining real-time network flow data containing multi-dimensional communication behavior records and protocol metadata of a target network system, and then extracting network behavior sequence features capable of describing communication behavior time relevance and a protocol interaction mode from the network flow data; inputting the network behavior sequence features into a pre-trained multi-modal threat prediction model, generating a potential attack vector prediction result containing an attack intention label, penetration path probability distribution and a threat level score, and dynamically verifying the potential attack vector prediction result based on a threat knowledge base, and finally, generating a self-adaptive defense strategy according to the penetration threat detection result, triggering defense interface deployment of a target network system, updating attack mode characteristics and a defense rule set of a threat knowledge base, and realizing intelligent and efficient network security penetration detection and defense.

A digital product network system includes a set of digital products each having a product processor, a product memory, and a product network interface. The digital product network system includes a product network control tower having a control tower processor, a control tower memory, and a control tower network interface. The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to generate product level data at the product processor, transmit the product level data from the product network interface, receive the product level data at the control tower network interface, encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products, and write the product level data structure to at least one of the product memory and the control tower memory.

A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost / benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost / benefit indicators.

A network system to use machine learning systems to create chaos testing scenarios on cloud-based applications. The system uses inputs from applications that are implemented on user computing devices to allow users to interface with a network or other system. The system creates a model of the application based on input data received from a network of applications, the model representing a structure, method, and dependencies of the application. The system identifies points of failure of the application and generates one or more chaos testing simulation scenarios that target the identified points of failure. The system performs the chaos testing based on the received simulation scenarios and logs the results of the testing. The system generates recommendations to revise code of the application based on the outcome of the chaos testing. A large language model may be used to provide documentation and analysis of the chaos testing.

A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost / benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost / benefit indicators.

The invention relates to the technical field of network security, in particular to a network security analysis method and system based on big data. Comprising the following steps: collecting related multi-source heterogeneous data of a network, and carrying out standardized processing such as cleaning and de-noising; network analysis is carried out based on the preprocessed data, network traffic is analyzed in real time by using machine learning and deep learning algorithms, and abnormal conditions are detected; constructing a risk prediction model according to a network analysis result and related information, and predicting a future network security risk level; if the risk level exceeds the threshold value, determining a security event source and a responsibility subject through data tracing; and finally, generating a safety response strategy according to risk prediction and data traceability results, and performing disposal. The corresponding system covers the modules of data acquisition, preprocessing, network analysis, risk prediction, data tracing, security response and disposal and the like, and all the modules work cooperatively to form a complete network security analysis and guarantee system, so that the stable operation of the network system is guaranteed.

Systems and methods are disclosed for implementing a virtual IP for a container pod. In certain embodiments, a method may comprise operating a cloud based network system in a containerized software environment to assign a virtual internet protocol (VIP) address to an application pod of a containerized software environment, the VIP being directly reachable from a network external to the containerized software environment. The method may include reserving a range of internet protocol (IP) addresses for use as VIP addresses, assigning a first fixed IP address to a first application pod, assigning a first VIP address from the range of IP addresses to the first application pod, and routing traffic directed to the first VIP address to the first fixed IP address.

Disclosed is a method for using deep reinforcement learning to optimize dependent task offloading in the Internet of Vehicles. The steps of the present invention comprise: S1, constructing a vehicle system network; S2, constructing a task model for an application; S3, constructing a task load model: on the basis of the vehicle network system and the task model, calculating time delay, power consumption, and paid incentive rewards for each of the following three offloading modes: local computing, offloading to a nearby vehicle, and offloading to a nearby roadside unit; S4, determining task priorities: first, on the basis of the allocation of predecessor nodes of sub-tasks in combination with a dynamic network environment, determining the priority of each sub-task, and then carrying out scheduling on the basis of a multi-queue algorithm; and S5, searching for an optimal offloading strategy by means of deep reinforcement learning. The method of the present invention does not require excessive prior knowledge, has good reusability in similar application scenarios, and has high practical value.

A resource allocation method for an unmanned aerial vehicle-assisted edge computing network based on intelligent reflecting surface assistance, comprising: constructing an unmanned aerial vehicle-assisted edge computing network system model based on intelligent reflecting surface assistance; constructing a communication model, a time delay model and an energy consumption model; according to the communication model, the time delay model and the energy consumption model, constructing a maximum average throughput function; optimizing the maximum average throughput function by using successive convex approximation and block coordinate descent algorithms, and according to the optimized maximum average throughput function, calculating an optimal resource allocation result. The present application uses an intelligent reflecting surface to assist in an unmanned aerial vehicle-assisted network system, thereby increasing the channel gain of links between automobiles and unmanned aerial vehicles.

The invention relates to the technical field of fault positioning, and is used for solving the problem that a fault event positioning system in the prior art has limitation and cannot adapt to dynamic changes. The invention provides a fault event positioning method based on topological model tracking analysis. The method comprises the following steps: S1, constructing a topological model based on system information and setting an edge weight; s2, tracking a fault propagation path by using time sequence data, and updating a path weight; s3, a fault propagation path is optimized through a shortest path algorithm, the shortest path algorithm adopts an A * algorithm, and a fault source is verified in combination with machine learning; and S4, outputting fault source nodes, related path information and confidence evaluation. The method is suitable for various complex network systems, is high in universality and expansibility, can adapt to the dynamic change of the topological structure and the running state of the system by dynamically updating the edge weight of the topological model, tracking the path and the machine learning model in real time, and always maintains high positioning accuracy and efficiency.

Provided herein are exemplary systems and methods for an intelligent secure networked system configured by at least one processor to execute instructions stored in memory, the system including a data retention system and an emotional analytics system, the emotional analytics system performing asynchronous processing to determine if interactions with a user's computing device are such that the user is responding from an emotional state of mind or a meditated state of mind.

The invention discloses a containerized resource dynamic allocation method for a power distribution network system and electronic equipment. The method comprises the following steps: judging that resource use data is greater than a preset resource threshold value by monitoring the resource use condition of each physical node in a power distribution network system; if yes, calculating a load balancing index of each physical node, and generating a container migration task list based on the load balancing index, the service priority and the service quality requirement information; packaging container data corresponding to the to-be-migrated container in the migration task list, and determining container state snapshot information and network connection information of the to-be-migrated container; establishing a secure channel between the source node and the target node based on the network connection information, and transmitting container data and container state snapshot information of the source node to the target node; reconstructing a container operation environment on the target node, and generating a target container; and suspending the to-be-migrated container on the source node, and activating the target container on the target node. And efficient migration of the container is realized.

Systems and methods are provided for determining a root cause of an incident that occurred in a 5G / 6G multi-access edge computing and core network system. In particular, the disclosed technology is directed to using a pre-trained generative model to determine the root cause for an incident as recorded in event data of a system log. The present disclosure generates a prompt for the generative model to determine the root cause for an incident as recorded in the system log. The prompt comprises a combination including event data from a system log, function hierarchy graph data, and function information as grounding information in a prefix of the prompt. The prompt further includes a question that requests determining a root cause of an incident as recorded in the event data. An answer as generated by the pre-trained generative model indicates of the root cause and a function call associated with the incident.

The invention relates to the technical field of resource scheduling control, and discloses a network resource hierarchical coordination method and system based on multiple agents. The method comprises the following steps: monitoring a network node load to obtain a resource state parameter; virtually constructing a resource pool and dividing a regulation domain; deploying an intelligent agent to construct a self-adaptive control system; extracting load characteristics to generate an analysis report; executing a double-layer adjustment strategy recording execution process; and analyzing the energy consumption data to optimize control strategy parameters. According to the application, the efficient utilization of network resources and energy consumption optimization can be realized while the performance of a computer network system is guaranteed.

A neural network system is provided. The system includes an autoencoder configured to encode input data into a latent space representation; a generator neural network configured to receive a noise vector and the latent space representation and output a set of routing coefficients; a discriminator neural network configured to evaluate the effectiveness of the routing coefficients by measuring the performance of a capsule network utilizing said routing coefficients; and a capsule network comprising a first capsule layer and a second capsule layer, wherein the routing coefficients are used to dynamically route outputs from the first capsule layer to the second capsule layer.

An intelligent reflecting surface assistance-based task unloading and resource allocation method for an unmanned aerial vehicle mobile edge computing network system, comprising: constructing an intelligent reflecting surface-assisted unmanned aerial vehicle mobile edge computing network system model; constructing an intelligent reflecting surface-assisted unmanned aerial vehicle mobile edge computing network communication model ; according to the communication model, obtaining a time delay model and an energy consumption model; establishing a multivariable joint optimization model so as to minimize the energy consumption of an unmanned aerial vehicle; and using a deep reinforcement learning method to solve the objective optimization model. By means of intelligent reflecting surface assistance, joint optimized unloading decision and resource allocation, the present application effectively reduces the energy consumption of the unmanned aerial vehicle and improves the performance of the unmanned aerial vehicle network system.

A method and system for protecting low-altitude airspace against unauthorized unmanned aerial vehicles by using a base station having communication and jamming functions. The present invention relates to the field of communications, unmanned aerial vehicles, and security supervision, and specifically to a method and system for managing unmanned aerial vehicles to protect low-altitude safety by using a wireless communication network base station to provide low-altitude wireless signal coverage comprising communication signals and jamming signals. "Low altitude, slow speed, and small size" unmanned aerial vehicles have become the main users of low-altitude airspace in China, but the safety supervision on the flight of unmanned aerial vehicles in low-altitude airspace is difficult to implement. By means of the present invention, the function of a jamming signal generator is integrated into a base station (base station having communication and jamming functions), and the effective coverage of communication / jamming signals in restricted areas can be realized in a cost-effective manner through wide deployment of base station network systems, imparting the restricted areas with a significant electromagnetic signal characteristic, thereby effectively preventing the intrusion of unauthorized unmanned aerial vehicles. In addition, the management and control of authorized networked unmanned aerial vehicles is implemented through networking and communication, providing technical means for the electronic management of unmanned aerial vehicles in low-altitude airspace.

The invention relates to the technical field of intelligent ship control, in particular to an intelligent ship moving control method and system based on a self-adaptive algorithm. The method comprises the following steps: acquiring hardware equipment data required by ship movement control; configuring a network topology structure of a computer network system, winch equipment and an encoder according to hardware equipment data, and constructing an electrical ring network; carrying out initialization setting and communication test on the electrical ring network, and establishing a data sharing mechanism between devices to obtain system initial configuration data; collecting the winding and unwinding length and tension parameters of a steel wire rope through a sensor of winch equipment based on initial configuration data of the system, and constructing a real-time monitoring model of the position and posture of the ship; and performing ship state monitoring by using the real-time monitoring model to generate ship real-time state data. According to the invention, the electrical ring network with a redundant backup mechanism is adopted, the communication real-time performance and reliability of the system are improved, and the stability of control signal transmission is ensured.

The present disclosure relates to a system to proactively detect in real time one or more threats in crowded areas. The present disclosure presents a proactive system for real-time threat detection in crowded areas. Utilizing a network of imaging sensors and advanced machine learning algorithms, the system identifies suspicious individuals, objects, and behavioral patterns within a predefined area. The system detects potential threats such as individuals on watch lists, suspicious objects like unattended bags, and abnormal behaviors indicative of security risks, by continuously monitoring and analyzing images and video feeds. Upon detection, the system promptly notifies authorities, providing detailed information on threat location, suspected individuals, and behavioral analysis. Privacy-preserving measures, including encryption of facial recognition data, ensure compliance with privacy regulations. The present disclosure offers a scalable, efficient, and automated solution to enhance security measures, reduce response times, and safeguard public safety in dynamic urban environments.

The invention relates to the technical field of power system monitoring and fault diagnosis, and discloses a partial discharge detection network system based on an adaptive attention mechanism. Comprising a data acquisition module, a signal preprocessing and time-frequency image generation module, a deep learning network of an adaptive attention mechanism, a fault classification and diagnosis module and a real-time monitoring and alarm module, the data acquisition module acquires a multi-mode partial discharge signal in real time, and preprocessing is performed through the signal preprocessing and time-frequency image generation module. The deep learning network of the adaptive attention mechanism carries out feature extraction and classification on the time-frequency image, finally, the fault classification and diagnosis module outputs a diagnosis result, and the real-time monitoring and alarm module carries out real-time monitoring and automatic alarm according to the diagnosis result. According to the system, multi-mode partial discharge signals are collected in real time, feature extraction and classification are carried out through a deep learning network, the attention degree of different features can be dynamically adjusted through a self-adaptive attention mechanism, and the accuracy and robustness of detection are improved.

The invention provides a network security threat intelligent identification method based on a generative large model, and belongs to the technical field of network security. Comprising the steps of obtaining system demand information, and determining a threat identification target and a system coverage range; collecting network system data, wherein the network system data comprises equipment log information, network flow information and terminal behavior information; preprocessing the network system data to obtain standardized data; selecting and training a machine learning model based on the standardized data to obtain a threat identification model; network behaviors are analyzed, abnormal behaviors are identified, and threat classification is carried out; executing corresponding safety protection measures according to the threat classification result; performing manual auditing on the abnormal behavior, and formulating a protection strategy in a targeted manner; and periodically evaluating the system operation state to generate a safety report. According to the invention, automatic response type intelligent identification of network security threats is realized, and the threats can be effectively detected under the condition of attack escape detection.

The invention discloses a multi-dimensional computing power dynamic perception routing decision-making method and system based on SRv6 driving. The method comprises the following steps: firstly, screening an optimal target computing power node based on a service demand and a node real-time load; and then, based on the dynamic network topological graph fusing the service sensitivity, calculating an optimal network path reaching the computing power node by using an improved SPFA algorithm, and realizing a global collaborative decision of selecting the most suitable computing point and searching the most efficient connection path. Meanwhile, based on an SRv6 driving service chain dynamic generation and closed loop execution method, a target computing power node and necessary network functions are abstracted into a programmable SID, an SID sequence (service chain) is dynamically constructed according to a double-stage decision result and is packaged in an SRH head, second-level path issuing and state monitoring are achieved through a programmable controller, and a real-time monitoring result is obtained. According to the method, the problems of poor real-time performance, single dimension, weak cooperative capability and the like in the prior art are solved, and efficient, stable and intelligent development of a future-oriented distributed intelligent computing network system can be promoted.

The invention discloses a multi-class network security threat perception and active and passive cooperative response processing system and method, and the system comprises a multi-class threat perception module which is used for perceiving a plurality of security threats existing in a network environment, and transmitting an obtained security event to a threat information association module; the threat information association module is used for analyzing and integrating various security events and extracting threat information from the security events; the attack graph-based threat path analysis module is used for mining vulnerability information in a network system, describing a network topology structure and an operation state, constructing an attack graph and obtaining active defense nodes in combination with a currently occurring security event and an analysis result of the attack graph; and the strategy generation module generates a corresponding response processing strategy according to the threat information and the property and the emergency degree of the security event, and generates an active defense strategy in combination with the active defense node, thereby realizing active defense and advanced deployment and control of potential threats. According to the invention, network security threats can be timely and effectively found and coped with.

The invention relates to the technical field of wireless communication, in particular to a wireless communication network system and method for intelligently fusing a multi-mode heterogeneous network. Comprising a multi-mode access unit; a protocol compatibility and conversion unit; the heterogeneous network intelligent fusion unit completes cross-network dynamic flow collaboration and resource optimization decision through cross-layer feature association mining, service-network two-dimensional weight calibration and a double-closed-loop feedback optimization mechanism, generates a resource scheduling instruction and transmits the resource scheduling instruction to the intelligent scheduling and resource control unit; an intelligent scheduling and resource control unit; and a system control and cooperation unit. According to the invention, the service-network two-dimensional dynamic fusion weight is constructed through the intelligent fusion unit of the heterogeneous network, and bandwidth, frequency spectrum and power resources are allocated according to the priority and margin balance principle in combination with the intelligent scheduling unit, so that the problem of insufficient two-dimensional coordination of resource scheduling is solved, the resource scheduling is matched with the service demand and the network load, and the resource scheduling efficiency is improved. And resource waste or incapability of meeting business requirements is avoided.

The invention discloses a diffusion model reasoning acceleration system and method based on model learning, and belongs to the field of artificial intelligence content generation, and the system comprises a step number cutting module which is used for compressing calculation steps of a denoising calculation process in diffusion model reasoning; and the task node matching module is used for distributing and scheduling the computing tasks to computing nodes of the cloud-side collaborative network system. According to the diffusion model reasoning acceleration system and method based on model learning, a cloud edge collaboration framework is provided, two strategies of step number compression and task scheduling are utilized, calculation acceleration under the condition that a user uses the diffusion model to execute a text graph reasoning calculation task is achieved, and the image quality can be guaranteed in the reasoning process.

A method for authenticated asset assessment is provided. The method involves executing a scan assistant on an asset to allow a remote scan engine to execute one or more scan operations on the asset for determining a state of the asset. The scan assistant may verify the identity of the scan engine by checking that a certificate received from the scan engine is signed with a private key associated with the scan engine. In some embodiments, the authentication may be performed as part of a TLS handshake process that establishes a TLS connection between the scan engine and the scan assistant. Once the scan engine is authenticated, the scan engine may communicate with the scan assistant according to a communication protocol to collect data about the asset. Advantageously, the disclosed technique reduces security risks associated with authenticated scans and improves the performance of authenticated scans.

The invention discloses an adaptive game-driven network defense method and system, and relates to the technical field of network security. The method comprises the following steps: generating a multi-modal bait according to network context information, and screening an optimal bait through credibility evaluation; constructing a time sequence feature tensor according to the network event sequence information of the bait, and obtaining predicted attack information by adopting a pre-trained attack prediction model; combining the network event sequence information of the bait and the predicted attack information to construct a defense income matrix, and carrying out iterative equilibrium solution to obtain an optimal strategy candidate pool; and taking the defense hybrid strategy of the optimal strategy candidate pool as an initial population, performing multi-objective optimization through a non-dominated sorting genetic algorithm to obtain a Pareto optimal strategy set, and performing screening to obtain an execution strategy set for dynamic defense decision making. According to the invention, the dynamic property, intelligence and self-adaptability of network defense are realized, and the ability of a network system to cope with complex attacks is effectively improved.

A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost / benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost / benefit indicators.