56298results about "Securing communication" patented technology

A network mapping behavior anomaly detection method and system based on machine learning is provided. The method includes: collecting dual-source traffic data, generating a structured log data set through dual-source log fusion engine; performing subgraph matching calculation to obtain a mapping behavior deviation degree; generating communication data containing a watermark identifier in a session corresponding communication path; verifying whether attack events carry the watermark identifier; generating a network mapping behavior anomaly detection report. According to the disclosure, an adaptive attack behavior model is constructed through a multi-modal feature vector based on structured logs and a graph protocol mapping rule base, so that the cognitive robustness to protocol camouflage and path drift is fundamentally enhanced, a real-time verification chain of detection results is built, and traditional passive detection is transformed into self-proof active defense through cross verification of watermark carrying state and behavior trajectory.

A coherent, intelligent, packet-switched memory fabric enables predictive, cache-coherent access across distributed compute, accelerator, and memory resources using a Memory-Fabric Transaction Layer Protocol (MF-TLP). MF-TLP defines routable packet formats for read, write, vectorized, atomic, reduction, collective, and predictive-prefetch transactions executed by memory-centric network interface controllers (MC-NICs). Each MC-NIC performs packet parsing, address translation, coherence management, and near-memory arithmetic or tensor operations while coordinating with MF-TLP-aware switches providing hierarchical directory control, multi-path routing, and in-network aggregation. Vectorized and multimodal packets encode multiple addresses or tensor offsets to reduce scatter / gather overhead, and programmable caching and quality-of-service modules manage tiered memory and tenant fairness. MF-TLP supports extension headers for predictive prefetch, collective coordination, and tenant governance, operating across hierarchical leaf-spine topologies using Ultra-Ethernet Transport, InfiniBand, or CXL fabrics. The system delivers scalable, low-latency, memory-centric orchestration for large-language-model training, multimodal AI, and data-intensive analytics.

The invention relates to the field of security surveying and mapping, in particular to a network security space surveying and mapping method, system and device based on multi-source data fusion, and the method comprises the steps: obtaining network security data in real time, and constructing a dynamic network topological graph; calculating a time-varying vulnerability score based on the topological graph and a historical attack log, and predicting an attack path and a propagation probability through a Bayesian network; performing cross-domain fusion on equipment, service and user behavior characteristics by adopting a federated learning framework to generate a dynamic asset portrait; generating a risk thermodynamic diagram in combination with spatial autocorrelation analysis and a multi-index fusion algorithm; a defense strategy effect is simulated based on an attack graph reconstruction engine, a Pareto optimal strategy combination is generated through an NSGA-II algorithm, and closed-loop verification and dynamic parameter correction are realized by utilizing honeypot deployment and flow traction. Therefore, the problems of topology update lag, single risk assessment dimension, cross-domain threat association fracture, defense strategy static stiffness, non-closed loop of a verification system and the like in the traditional technology are solved.

The present disclosure relates to systems and methods for facilitating interoperability between blockchain networks to facilitate bridging of non-fungible tokens (NFTs) across chains. In embodiments, a method includes receiving a bridging request at a first bridge smart contract to transfer a source NFT from a first blockchain to a second blockchain. Upon verification, the first bridge smart contract locks the source NFT. An oracle server determines whether a clone NFT associated with the source NFT exists on the second blockchain. If so, the oracle instructs a second bridge smart contract to unlock the clone NFT. If no clone NFT exists, the oracle instructs minting of the clone NFT and assigning ownership of the clone NFT to the user's account on the second blockchain. The clone NFT remains controllable by the user while the source NFT stays locked on the first blockchain, maintaining a one-to-one relationship between the tokens across chains.

Blockchain synchronization and point-of-sale integration systems are provided in a packet-switched computer network. The POS interfaces through the network with a consumer smartphone and a blockchain validation device. The POS shares a session identifier with the blockchain validation device by transmitting a graphic encoding of a payment request and the session identifier to the smartphone. The camera of the smartphone is configured with an image of the graphical encoding. A payment instruction is encoded to conform to the payment request including the session identifier and is transmitted to the blockchain validation device. The blockchain validation device performs a verification of the correctness and authenticity of the payment instruction encoded in the data record. The POS device receives a confirmation that the correctness and authenticity of the payment instruction encoded in the data record to confirm it has been verified.

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.

The invention relates to the technical field of communication scheduling, discloses a communication scheduling network management intelligent optimization system and method based on AI dynamic decision, and solves the problems of insufficient scheduling dynamics, closed loop deficiency and poor edge adaptation in the prior art. Comprising a multi-dimensional data fusion acquisition module, a dynamic AI decision engine module, a cross-domain collaborative scheduling module and an intelligent closed-loop feedback optimization module. The dynamic AI decision engine module evaluates business value and resource pressure based on an edge-center collaborative architecture, predicts transmission quality and quantifies strategy income, the cross-domain collaborative scheduling module realizes intra-domain resource slicing and inter-domain strategy negotiation and path optimization, the intelligent closed-loop feedback optimization module constructs a data closed loop to iteratively optimize model parameters, and the dynamic AI decision engine module performs multi-domain collaborative scheduling on the basis of the edge-center collaborative architecture. Intelligent scheduling and autonomous optimization of network resources are realized, and the real-time performance, the reliability and the resource utilization rate of a communication network are improved.

An AI-based Cybersecurity System and Method enable real-time detection, analysis, and mitigation of cyber threats within computing networks using adaptive artificial intelligence. The system continuously monitors network traffic, extracts behavioral and contextual attributes, and applies deep learning-based inference to identify anomalous activities indicating security breaches. The method integrates several computational units, including a network monitoring unit, feature extraction unit, artificial intelligence processor, contextual reasoning processor, and decision synthesis unit, to compute a composite risk index quantifying threat likelihood and severity. A classification processor categorizes detected threats into types such as ransomware, phishing, or unauthorized access, while a mitigation control processor initiates automated response actions to isolate compromised nodes and restore network integrity. An adaptive learning processor updates AI models using feedback from confirmed incidents. This provides a scalable, self-evolving cybersecurity framework that minimizes human intervention and enhances resilience against dynamic and zero-day threats.

The invention relates to the technical field of data security management, in particular to a self-adaptive data security management and risk early warning system based on intelligent analysis under a cloud platform. Comprising a multi-dimensional data acquisition module; an intelligent analysis module; a self-adaptive strategy generation module; a risk early warning module; and a user behavior portrait construction module. In the design, the security policy can be dynamically adjusted along with the risk situation of the cloud platform, the problem that a static policy cannot adapt to real-time change is solved, and dynamic mapping of risk characteristics-policy parameters is realized; according to the design, the one-sidedness of single-dimension analysis is broken through, multi-modal feature association modeling of user behaviors is achieved, an abnormal behavior triggering threshold value is accurately recognized, and the integrity and accuracy of risk feature analysis are improved; the security policy can be continuously optimized through historical event data, so that protection efficiency attenuation caused by long-term static operation is avoided, and an autonomous lifting link of data driving, algorithm optimization and policy evolution is realized.

The invention provides an analyzable anti-attack network security method and system based on an AI unified model. The method comprises the following steps: S1, carrying out attack source tracing and attack mode identification on an input data stream; s2, performing protocol structure analysis and grammar element extraction on the input data stream in a grammar verification layer, and starting a grammar rule matching process to obtain a grammar exception perception set; s3, fusing attack vector information on the basis of a grammar anomaly perception set in a semantic analysis layer, constructing a semantic relation graph, and outputting a semantic risk vector; s4, taking the semantic risk vector as input, combining a business scene, resource constraint and strategy preference, modeling a defense target, and outputting an optimal response path; and S5, forming a model evolution path based on local feedback and global collaboration. Through a three-layer full-information analysis mechanism and behavior feedback driving, interpretable recognition of attack intentions and collaborative optimization of defense paths are realized, attack recognition is comprehensive, response decision is accurate, and strategy evolution is controllable.

The invention provides an intelligent Internet of Things public security management and control system and method based on multi-source data fusion, and belongs to the technical field of Internet of Things security. According to the system, unified collection and standardized processing of multi-source data are achieved by recognizing a system operation scene and loading corresponding model parameters and response strategies, and a unified semantic representation structure is constructed. And the system executes anomaly detection at an edge node, completes risk scoring and grading alarm judgment in combination with a semantic structure and anomaly feature information, and generates a control response instruction based on strategy matching. Meanwhile, dynamic optimization of the scoring model and identity authentication, behavior auditing and data compliance export in the operation process are supported. The system has intelligent identification, adaptive analysis and response closed loop capabilities, and improves the precision and credibility of public security management in the Internet of Things environment.

Techniques and systems for the dynamic generation of a map interface include generating temporal activity models by representing individual social media postings as having respective density distributions in time. Each posting's temporal density distribution spans multiple sequential time windows centered on the posting's timestamp, with density contributions decreasing in value for time windows further from the timestamp. The temporal models may be combined with spatial density distributions to generate comprehensive geo-temporal representations of social media activity. A graphical user interface displays an interactive map with overlay elements determined based on calculated activity attributes, including detected temporal patterns and anomalies identified by comparing current activity models against historical baselines. The modeling approach enables improved visualization of activity patterns while providing inherent privacy protection through probabilistic representation of individual posts.

Methods, apparatuses, and products for providing a code analysis feedback loop for code created using generative AI, including: performing a code analysis on code generated by a generative AI model; determining, based on the code analysis, whether the code satisfies one or more conditions; responsive to the code failing to satisfy the one or more conditions, until the code satisfies the one or more conditions, repeatedly updating the code by receiving, from the generative AI model, updated code based on a previously performed code analysis; and providing the code generated by the generative AI model.

The invention discloses an information security management method based on data processing, and particularly relates to the field of information security management, comprising multi-source heterogeneous data acquisition, multi-dimensional feature index calculation, comprehensive threat detection calculation and adaptive threat decision. According to the method, the covert communication channel is accurately identified by constructing the encrypted traffic multi-mode detection model and combining protocol fingerprint entropy and traffic self-similarity analysis; an improved entropy weight fusion algorithm is adopted, collaborative analysis of system process abnormity, network behavior offset and hardware degradation is achieved, and a cross-dimension attack chain is reconstructed; a storage medium physical feature-cryptographic module runtime joint monitoring mechanism is created for the first time, hardware layer physical attacks and cryptographic side channel vulnerabilities are synchronously defended through temperature-delay correlation evaluation and key derivation density analysis, a complete attack surface from a physical layer to an application layer is covered, and the security is improved. And the detection precision and the response real-time performance of the complex threats are obviously improved.

The invention relates to the technical field of server upgrading, and discloses a server firmware remote upgrading method and system, and the method comprises the steps: a management node obtains a device identifier list and target firmware version information of a to-be-upgraded server, and uses a distributed task scheduling algorithm to divide tasks; transmitting an upgrading instruction and a data packet through an encryption channel, and generating an incremental packet by using a differential upgrading technology; data transmission is guaranteed through heartbeat detection and breakpoint continuous transmission; after pre-verification of the server-side security sandbox, upgrading and isolating tasks in an idle period; verifying by adopting a gray release strategy, and automatically rolling back if the verification fails; and storing an upgrade state and a log by means of a block chain network. The system comprises corresponding function modules. According to the method, the upgrading efficiency, safety and reliability are improved, the influence on services is reduced, data cannot be tampered and can be traced, the problems existing in a traditional upgrading mode are effectively solved, and the method is suitable for various server firmware remote upgrading scenes.

An example operation includes one or more of connecting a local user profile hosted within a software application hosted by a first platform with a profile hosted by a data source on a second platform, extracting a first set of profile features from the profile hosted by the data source and a second set of profile features from the local user profile, determining a new profile feature to add to the local user profile based on the execution of an artificial intelligence (AI) model on a combination of the first and second sets of profile features, and displaying information about the new profile feature on a user interface of the first or second platform.

The invention relates to the technical field of network security, in particular to a network security big data state evaluation method based on pattern recognition, which comprises the following steps of: extracting multi-modal features from a network flow log, a system event log, a host behavior log and threat intelligence data, generating a feature matrix, performing feature dimensionality reduction by adopting an auto-encoding network, and obtaining a network security big data state evaluation result; carrying out attack behavior classification and abnormal mode identification in combination with unsupervised clustering and a graph neural network; constructing an attack transition probability matrix based on a Markov model; forming a time sequence attack chain; predicting an attack development trend; and a dynamic protection instruction is issued to the safety equipment. According to the method, the unknown attack detection capability can be improved, the time sequence attack traceability is enhanced, the security situation assessment is optimized, and the method is suitable for security situation awareness in cloud computing, industrial internet and large-scale network environments.

The invention provides a network security protection method and system applied to regional digital and intelligent asset businesses, and the method comprises the steps: collecting asset data flows of a plurality of asset business nodes in a target region, carrying out the threat feature extraction of the asset data flows based on a preset threat knowledge graph, and obtaining a threat feature extraction result; generating a dynamic threat feature vector corresponding to the asset service node; inputting the dynamic threat feature vector into a pre-trained dynamic protection model, and outputting a real-time protection strategy adaptive to the asset service node through a multi-layer decision network in the dynamic protection model; performing strategy execution on the network flow of the asset service node based on the real-time protection strategy, generating a strategy execution result and feeding back the strategy execution result to the dynamic protection model; and performing adaptive optimization on decision parameters of the dynamic protection model according to a strategy execution result, and generating an updated dynamic protection model for a protection decision of a next round of asset business nodes.

The invention discloses a security event log real-time monitoring and analyzing system and method for a power grid communication network, and relates to the technical field of network security management. The causal relationship graph building module is used for building a causal relationship graph of the target power grid communication network; the multi-dimensional correlation analysis module is used for collecting and analyzing multi-source heterogeneous log data in real time; the abnormal security event identification module is used for identifying an abnormal security event according to the causal relationship graph and the multi-dimensional correlation analysis result; and the attack chain tracking response module is used for tracking the attack chain. According to the method, the technical problem that the existing power grid communication network security monitoring lacks tracking of abnormal event evolution from the time dimension and cannot accurately identify and track a multi-stage attack chain is solved, and the effects of dynamically tracking the evolution process of the abnormal event and identifying a potential attack chain by establishing a time causal chain graph are achieved. And the detection precision and the response speed of the attack behavior are improved.

A method is provided for encrypted network traffic analysis. The method includes capturing network traffic data; calculating entropy of said data to classify traffic as encrypted or non-encrypted; applying statistical and sequential feature hybridization on encrypted traffic to extract comprehensive features; analyzing the features using a neural network model to identify encrypted traffic types and detect anomalies; and refining the analysis based on entropy and neural network insights through a feedback loop.

The invention relates to the technical field of archive management, in particular to an archive full life cycle management system and method based on cloud computing. The method specifically comprises an intelligent classification module based on a format adaptation degree function and semantic density analysis, a five-stage life cycle management module, and an authority scheduling module fusing RBAC / ABAC authority control and block chain auditing, and based on a matrix decomposition archiving static coding mechanism, document security storage and analysis are realized through an orthogonal matrix and pseudo-inverse operation, and the file security is realized. The data security in a long-term archiving scene is improved; cloud storage resources are dynamically optimized according to the document life cycle state and the access frequency, and the storage cost is reduced in combination with a cold and hot layering strategy; and an AI algorithm is introduced, the archive retention value is automatically evaluated according to laws and regulations and semantic analysis, and a compliant destruction decision is realized. According to the method, through multi-model fusion metadata extraction, dynamic strategy binding and automatic state transition, the whole-process management efficiency and safety are remarkably improved.

The invention provides an industrial control network security advanced threat detection system fused with artificial intelligence. The system comprises a multi-source data acquisition module, an intelligent analysis engine, a threat detection module, a dynamic defense module and a self-evolution learning system which perform data interaction in sequence. The industrial control network security advanced threat detection system fused with artificial intelligence realizes collaborative decision-making among the modules through a dynamic knowledge graph. Through multi-source data fusion, dynamic knowledge graph and lightweight model design, the core problems of protocol analysis, threat association, defense collaboration and model adaptability in the industrial control network security field are solved, and a full-stack protection system covering'perception-analysis-decision-response-evolution 'is constructed. The deep analysis capability of an industrial protocol is improved, the dynamic threat association analysis is broken through, the agility of a defense strategy is enhanced, and the feasibility of continuous optimization of a model is improved, so that a systematic solution is provided for advanced threat defense in a complex industrial control environment.

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

Guiding query creation for a generative artificial intelligence (AI)-enabled assistant, including: receiving, via a natural language interface for a security framework monitoring a cloud deployment, a natural language input comprising one or more entity identifiers; gathering information based on the one or more entity identifiers; providing, to a generative artificial intelligence (AI) model, a prompt based on the natural language input and comprising the gathered information; and receiving, from the generative AI model, a response to the prompt.

Systems and methods for customer environment integration with anomaly detection systems are disclosed. The methods include receiving cloud computing environment data for the cloud computing environment; generating, based on the received cloud computing environment data, a set of commands for generating cloud computing infrastructure for anomaly detection, the set of commands including one or more commands for generating a first set of anomaly detection infrastructure elements within the cloud computing environment and for generating a second set of anomaly detection infrastructure elements at the anomaly detection system; and initiating an execution of the set of commands.

Architecture for a generative artificial intelligence (AI)-enabled assistant, including: receiving, via a natural language interface for a security framework monitoring a cloud deployment, a natural language input; selecting, by a delegator generative artificial intelligence (AI) model and based on the natural language input, a particular generative AI model from a plurality of selectable generative AI models; prompting the particular generative AI model based on the natural language input; and providing, via the natural language interface and in response to the natural language input, a response from the particular generative AI model.

The invention provides a network traffic anomaly detection model training method and device and a readable storage medium, and the method comprises the steps: extracting a traffic statistical feature vector according to original network traffic data, and generating an initial mixed data set; generating a confrontation disturbance sample output enhanced feature matrix based on the initial mixed data set; constructing a self-adaptive feature fusion rule based on the enhanced feature matrix, embedding asset association degree parameters into an attention calculation layer of a feature encoder, and outputting encoding features fusing threat intelligence; inputting the coding features fused with the threat intelligence into a pre-constructed initial detection model, generating false report and missing report correction labels based on the suspicious traffic fragments, and outputting an adversarial sample correction data set; and performing adversarial training on the initial detection model through the adversarial sample correction data set to obtain an incremental detection model for network traffic anomaly detection. According to the invention, the detection precision, the anti-interference capability and the real-time defense response capability of the detection model to novel attacks can be improved.

The invention relates to the technical field of operation and maintenance management and control, and discloses an intelligent building remote operation and maintenance management and control system based on a large model and a cloud edge collaborative architecture, and the system comprises a configuration subsystem which is used for carrying out the reverse proxy and persistent connection configuration of edge gateway equipment in an intelligent building, and obtaining a TLS encryption bidirectional communication channel; the construction subsystem is used for constructing a cloud-edge-end three-layer cooperative computing architecture; the conversion subsystem is used for performing unified semantic abstraction and conversion on the heterogeneous protocol data to obtain semantic data in a standard JSON (JavaScript Object Notation) format; the rule processing subsystem is used for performing rule processing through a near-end decision engine of the edge gateway to obtain a local intelligent response result; the multi-dimensional analysis subsystem is used for carrying out multi-dimensional analysis through a cloud large model engine to obtain an equipment health score, a fault prediction result and an energy optimization strategy, seamless fusion and interoperation of heterogeneous system data are achieved through the system, and then global optimization and linkage control of the intelligent building are achieved.

A modular surgical system for use in a surgical procedure is disclosed. The modular surgical system includes a control module including a power supply and surgical modules including a first surgical module and a second surgical module. The first surgical module is arranged in a stack configuration with the control module and the second surgical module. The power supply is configured to output power to the first surgical module and the second surgical module. A power backplane is configured to deliver the power to the first surgical module and the second surgical module through the first surgical module. A control circuit is configured to adaptively adjust power allocations to the first surgical module and the second surgical module.

The invention provides a network security defense method and system based on intrusion modeling trapping, and the method comprises the steps: constructing a dynamic virtual asset atlas based on a real network topology, generating a virtual node and an interaction link through protocol fingerprint confusion, implanting an interface deformation rule in combination with a session context, and constructing a protocol stack controllable dynamic trapping interface. Extracting topology correlation degree parameters, triggering node recombination and attack path shunting rules, using a protocol stack mirror image tunnel confusion instruction flow path, constructing a dynamic attack path confusion network, and directionally transmitting an intrusion instruction to a trapping interface protocol stack. And when the parameters reach a threshold value, a node recombination strategy and an interface deformation rule are updated, a dynamic attack and defense game mechanism is formed, the attack behavior induction and feature capture capability is enhanced, and active interference and accurate traceability of network intrusion behaviors are realized. According to the technical scheme provided by the embodiment of the invention, active shunting of the attack path and accurate induction of the trapping interface can be realized, and the attack detection rate is improved.