33 results about "Critical infrastructure" patented technology

The invention belongs to the technical field of network security, and discloses a countering measure strategy construction method and system based on attack intelligence. According to the method, firstly, multi-source attack intelligence is fused, and a causal time sequence attack knowledge graph is constructed; then, mapping the atlas on a digital twinborn model based on the IEC 62443 standard, constructing a Bayesian attack graph, and quantifying the risk of each attack path; then, taking the risk, the cost and the operation influence as multiple targets, and adopting an NSGA-II algorithm to generate a Pareto optimal countering strategy set; further, an optimal robust strategy is selected from the strategy set by solving the Stackelberg safety game model; and finally, explaining the decision process by using an interpretable AI technology. According to the method, the problems of passive threat intelligence analysis, static risk assessment, sub-optimal strategy selection and opaque decision-making process in the prior art are solved, and active, quantitative, optimal and credible defense decision-making for the key infrastructure is realized.

In one aspect, a computerized system of an Energy Yield Management Software (EYMS) framework includes an Industrial Grade Solar Microgrids (IGSM) deployment comprising a control unit configured to communicate with a power generating source, collect data from the power generating source, and issue instructions to the power generating resource. The control unit is further configured to communicate with a sensor, an automation module, a local load, an energy storage systems, or a generation resource within a customer IGSM deployment. The EYMS is configured to communicate through a communication network to the IGSM deployment. A Digital Twin configured to actively use real time and historical data to learn how each component in the IGSM performs under a plurality of operational conditions and characteristics, wherein a predictive model is employed by the Digital Twin for a prediction operation, an optimization operation and a prescriptive maintenance operation of the IGSM.

A system and method for analyzing cybersecurity posture for an OT infrastructure includes categorizing a plurality of devices of one or more plants into levels, based on an exposure of each device to a communication network, identifying CVEs of components of the plurality of devices; assigning a severity value to the one or more CVEs of components and determining a plant cybersecurity posture score for the one or more plants; computing a critical infrastructure cybersecurity posture score for the OT infrastructure; and applying remediation to one or more vulnerable components based on a prioritization sequence.

This disclosure describes systems, methods and workflows; computer-implemented methods; computer software products; and ML models for real-time monitoring of the infrastructures in modern cities, including bridges, road tunnels, airports, buildings, and wind turbine towers, utilizing seismic data. The monitoring is not restricted solely to the recorded seismic data. This disclosure also emphasizes the importance of monitoring various critical infrastructures using seismic sensors, including security sites and manufacturing facilities, to enhance safety and security, detect infrastructure health, and prevent unauthorized intrusions. Additionally, seismic sensors can be used to monitor indoor human activity. Bridge monitoring involves continuous assessments to ensure structural integrity and longevity, while road tunnel monitoring focuses on optimizing traffic conditions and monitoring structural images. Monitoring at airports improves runway safety and ensures optimal performance of the aviation infrastructure, minimizing potential risks and enhancing overall safety and efficiency in air travel. Deploying seismic sensors to monitor a building's infrastructure enables the detection and location of potential risks to the building structures. Monitoring wind turbine towers is crucial for optimizing green energy production and ensuring the structural health of the turbines. Security and manufacturing sites are monitored to ensure the safety and security of the facilities and prevent unauthorized intrusions. By monitoring and analyzing seismic data of human activities in rooms, one can identify different types of activities and detect instances of walking and falling. By employing seismic sensors and real-time processing of the seismic data, these monitoring systems collectively contribute to the safety, security, resilience and reliability of vital infrastructures in various domains.

This invention provides a heuristic dynamic path optimization method for physical protection systems in a three-dimensional scene, comprising the following steps: Through three-dimensional path direction planning, beacon point setting, and detection point distribution estimation based on a geometric model, a perspective representation of virtual interwoven profiles between different floors in a virtual three-dimensional space is established based on the structural and connection characteristics of upper and lower floors of a building in three-dimensional space. The three-dimensional path planning direction is determined through adjacent grid cell mapping of each level profile, and agent recognition is achieved using ray detection functionality in Unity. Based on beacon points and detection probability estimation based on a geometric distribution model, a three-dimensional heuristic reverse path planning algorithm is constructed to achieve dynamic path planning and navigation for adversaries or agents. Based on the EASI model theory, the system interception probability is dynamically searched and calculated along the path planning direction to effectively identify the weakest path in the system, which is used for the optimization and improvement of critical infrastructure entity security protection design.

This invention relates to the field of navigation and timing technology, specifically to a GNSS timing data prediction method and related equipment. The method involves acquiring Roland timing data when GNSS timing data is invalid and preprocessing it. The preprocessed data is then input into a trained single-hidden-layer extreme learning machine model. This model calculates the hidden-layer output matrix by randomly initializing the input layer weight matrix and bias vector, and solves for the output weights using regularized least squares. The model outputs a normalized GNSS timing data prediction value, which is then denormalized to obtain the final GNSS timing prediction result. This method combines the high stability of the Roland system with the efficient computational power of the extreme learning machine to achieve accurate GNSS timing data prediction. It is suitable for enhancing the resilience of positioning, navigation, and timing systems in critical infrastructure scenarios, ensuring the continuous operation of the system even when GNSS signals are interfered with or fail.

Various techniques for providing artificial intelligence-enabled (AI-enabled) device ownership identification for securing nationwide critical infrastructure systems are disclosed. In some embodiments, a system / process / computer program product for providing artificial intelligence-enabled (AI-enabled) device ownership identification for securing nationwide critical infrastructure systems includes discovering vulnerable devices across a plurality of networks; automatically identifying device owners using a large-language model (LLM); and automatically enriching the discovered vulnerable devices with sector, location, and point of contact (POC) information.

Systems and methods for mathematical modeling of the hardware and software stack of commodity computer platforms are provided, enabling provable guarantees on memory, device, and program execution. This approach addresses the technical problem of reliance on system agents that rely on implicit trust in the operating environment, which can be exploited by sophisticated attackers using complex threats such as memory access exploits and code / data integrity exploits. The solution provides a proactive, mathematically-backed security solution that eliminates entire classes of cyberattacks by design, ensuring realizable guarantees on commodity computer platforms running hardware and software stack elements at the lowest operating level. This approach has significant advantages over current reactive cybersecurity methods, including reduced complexity and overhead, and increased confidence in the integrity of the system. The solution's main uses include providing mathematically-backed security and availability guarantees for critical infrastructure, financial institutions, and other organizations vulnerable to cyberattacks.

The invention discloses a key infrastructure network attack tracing method based on dynamic risk assessment, and particularly relates to the technical field of network and information security, session watermarks are generated through an entrance side, source aperture records are unified, a session chain is formed through cross-domain splicing, and a command sequence and assets are aligned; auditing records and measurement are extracted on the equipment side according to the command track, fluctuation irrelevant to the command is eliminated through adjacent comparison, and stable equipment response is obtained; a window alignment robust index and a command effect sensitive index are put forward, a discrimination rule is synthesized to generate a stripping sequence basis, commands are imported piece by piece in an isolation replay environment, a minimum command subset is selected in combination with a unified trade-off factor, and causal matching is verified through dynamic sorting of stripping strength coefficients. A matching result is backfilled to generate a responsibility-accounting label, settlement is performed to a dynamic risk vector, a disposal priority is formed, and the whole process can be audited and rechecked; the method solves the problems of evidence splitting, cause and effect unconfirmation and unstable responsibility of key infrastructures in a multi-hop environment.

A novel, two-stage Distributed Fiber Optic Sensor (DFOS) placement strategy and method that ensures resilient monitoring of critical infrastructure during electrical power supply failures. This strategy and method combines a heuristic algorithm, PURE (Power Source-aware Route Exploration), with Integer Linear Programming (ILP) optimization. The PURE algorithm explores potential DFOS routes while explicitly considering the dependency of DFOS devices on the electrical power distribution network, ensuring routes electrically powered by the same electrical power feeder are disjoint. Subsequently, the ILP selects the optimal set of DFOS placements to minimize the total number of deployed sensors while meeting critical infrastructure monitoring requirements, such as redundant monitoring for high-importance links. The method enhances the observability of critical links, achieving 100% monitoring coverage for important links during simulated power outages affecting an electric feeder.

The invention discloses a network security anti-fact defense method based on multi-agent collaboration, which comprises the following steps: deploying local defense agents with autonomous decision-making capability in different areas of a network, and setting a core defense agent oriented to global collaboration to carry out unified coordination and control on a multi-point linkage security policy; therefore, cross-region and cross-security domain cooperative defense is realized. The method is suitable for a power secondary system, an industrial control system, an industrial internet and other key infrastructure networks, and can also be popularized and applied to a cloud computing data center, a government affair private network and other network environments with complex topology and strict service continuity requirements. And the technical problems that an existing static boundary isolation and access control method lacks dynamic description capability on the influence of the service running state, the attack evolution process and cross-site linkage, and a timely, fine and interpretable defense decision is difficult to realize when the attack is quickly diffused or the service topology is frequently changed can be solved.

A computer-implemented security architecture prevents compromise of an artificial-intelligence workload, agent, model-orchestration platform, or application server from automatically becoming an enterprise-wide data breach, unrestricted semantic reconstruction, strategic-intelligence extraction, unauthorized cross-domain correlation, or unauthorized externally effective operation. Enterprise information is partitioned among independently controlled identity, content, relationship- mapping, and optional cryptographic-material domains such that protected components remain Technically Non-Joinable outside an authorized reconstruction session. A non-bearer Reconstruction Authorization Object (RAO) binds permitted fields, permitted association scope, authorized purpose, execution context, attested workload identity, tenant, session, policy epoch, jurisdiction, recipient, destination, disclosure conditions, and output scope to protected authorization state. Approved components are released as session-bound components and reconstructed only within a Protected Reconstruction Domain to create an ephemeral minimum-necessary representation without persistently reconstructing a complete semantically usable enterprise record outside the protected processing span. Where an artificial-intelligence server, autonomous agent, retrieval pipeline, model-orchestration framework, tool-use environment, vector-store interface, or application server is compromised, an attacker may attempt to reconstruct customer data, financial records, research assets, source code, intellectual property, product-development information, communications, operational intelligence, or strategic enterprise relationships. However, compromise of computation does not itself provide reconstruction authority, semantic association authority, or Release Authority. Generated Candidate Acts and Candidate Outputs remain sealed, capability-restricted, confined to protected state, or otherwise Non-Releasable while current execution-context correspondence, provenance continuity, permitted association scope, revocation state, policy epoch, destination, recipient, disclosure budget, inference-channel budget, and jurisdiction conditions are independently verified. A Protected Output Validation Receipt is committed before an output-specific Output Release Capability is generated, such that only a designated Output Release Boundary can render, transmit, invoke, store, commit, execute, or otherwise effectuate the verified result. The disclosed architecture establishes Execution–Consequence Decoupling, Mandatory Mediation, Technical Non-Joinability, and Technical Non-Completability, enabling enterprise artificial intelligence to compute without independently acquiring authority to disclose protected enterprise relationships or produce unauthorized external consequences. The architecture is applicable to enterprise AI, agentic AI, multi-agent systems, model-serving infrastructure, retrieval-augmented generation, vector databases, cloud computing, confidential computing, cybersecurity, telecommunications, financial services, healthcare, government systems, industrial automation, critical infrastructure, robotics, autonomous systems, and cyber-physical environments.

This application relates to the field of cybersecurity technology and discloses a cross-domain collaborative emergency response and attack suppression method for critical infrastructure. The method includes: collecting and correlating security data from at least two independent security domains; performing identification and correlation analysis using a graph neural network model to generate a fused security posture describing a cross-domain attack chain; based on this fused security posture, generating an optimal collaborative response strategy with specific defensive actions and execution sequences through a preset attack-defense game model; parsing the strategy into layered security instructions to drive security execution devices within the target security domain to perform configuration operations; and coordinating security execution devices from multiple security domains to jointly address the cross-domain attack chain based on the configuration operations. This invention enables overall perception, intelligent collaborative decision-making, and integrated linkage suppression of high-level, cross-domain network attack chains, effectively improving the overall defense capabilities of critical infrastructure.

This invention discloses a trusted time synchronization device and method, relating to the field of trusted time synchronization technology, including a trusted time management platform and a trusted time synchronization client. The trusted time management platform is used to complete platform identity authentication, service activation, time synchronization message and service management, real-time control of the time synchronization client, and time synchronization billing and evidence storage management. The trusted time synchronization client, based on dedicated hardware and a trusted execution environment, completes certificate application, login management, status reporting, time synchronization and metering management to achieve trusted time synchronization and accurate metering. This invention constructs a new trusted time synchronization system that is highly secure, verifiable, traceable, and operable, meeting the high reliability and commercial operation requirements of critical infrastructure for time benchmarks in the digital economy era.

Critical infrastructure (CI) blueprint selection for optimized response to state changing conditions includes defining a hierarchy of interdependent CI elements of a community in a digital twin and receiving characterization data for different nodes of the digital twin. A fingerprint is generated for the hierarchy based upon the characterization and a blueprint which corresponds to the fingerprint retrieved from a data store, the blueprint defining a set of parameters to be applied to different computing elements of different nodes in the hierarchy determined to minimize a cascading effect of a state changing condition in one of the nodes. Finally, the set of parameters of the retrieved blueprint is applied to corresponding computing elements of the digital twin and a state changing condition is simulated in the digital twin in a selected node so as to compute the cascading effect of the state changing condition to other nodes dependent upon the selected node.

A novel, two-stage Distributed Fiber Optic Sensor (DFOS) placement strategy and method that ensures resilient monitoring of critical infrastructure during electrical power supply failures. This strategy and method combines a heuristic algorithm, PURE (Power Source-aware Route Exploration), with Integer Linear Programming (ILP) optimization. The PURE algorithm explores potential DFOS routes while explicitly considering the dependency of DFOS devices on the electrical power distribution network, ensuring routes electrically powered by the same electrical power feeder are disjoint. Subsequently, the ILP selects the optimal set of DFOS placements to minimize the total number of deployed sensors while meeting critical infrastructure monitoring requirements, such as redundant monitoring for high-importance links. The method enhances the observability of critical links, achieving 100% monitoring coverage for important links during simulated power outages affecting an electric feeder.

The present invention relates to an autonomous agentic artificial intelligence based defense system for protecting critical infrastructure from cyber threats through continuous, validated, and adaptive security operations. The system employs distributed processing units configured to analyze operational telemetry, network communication data, and control signals in real time to identify deviations from established infrastructure behavior. Artificial intelligence based characterization logic is applied to correlate detected deviations with stored threat profiles and infrastructure parameters, followed by multi-stage validation to confirm threat authenticity and severity. Adaptive learning mechanisms dynamically update characterization thresholds and validation parameters based on historical incidents and evolving attack patterns, enabling continuous improvement without manual reconfiguration.

The invention provides a low-code development framework and method based on micro-service, relates to the technical field of computers, and solves the technical problems that in the prior art, diversified software development requirements of enterprises are difficult to meet, and resource conflicts, deployment coupling and service avalanche are likely to occur in the software development process. The framework specifically comprises service support design, micro-service design, storage design and deployment design. The service support design is used for providing key infrastructure and public service for the micro-service system; the micro-service design is used for constructing a series of small micro-services capable of independently bearing a service function based on a service field and a responsibility boundary; the storage design is used for selecting an adaptive storage scheme according to data characteristics and business requirements; the deployment design is used for supporting deployment and operation of the micro-service application under different operating systems. The method is used for secondary development of software products and customized software.

1. Name of the product in this design: Wide-beam anti-drone equipment. 2. Application of this design: It is used in temporary major security tasks, border areas, critical infrastructure and other occasions where drone control is required, and can effectively deal with various types of drones. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

This invention discloses an artificial intelligence (AI) secure operation system and method based on a hardware unidirectional data channel and a network-free kernel. The system includes a data access unit, an isolated AI computing unit, a hardware unidirectional data channel, and a security management terminal. The hardware unidirectional data channel enables unidirectional data transmission from the data access unit to the isolated AI computing unit at the physical layer. The isolated AI computing unit runs an operating system kernel stripped of general network communication protocol stacks, preventing processes running within it from actively establishing external network connections. By combining a hardware-level unidirectional transmission structure with kernel-level network capability limitations, an AI operating environment with unidirectional data input is constructed. This invention also supports collaborative computing with remote computing nodes through a controlled encrypted tunnel under the control of the security management terminal, and can be used in high-security AI deployment scenarios such as finance, government affairs, and critical infrastructure.

A dynamic Stand-Alone Power System (SAPS) formation optimization system is developed to facilitate resilience in large utility sites with complex networks connected to multiple substations. The system ensures uninterrupted power to critical loads during substation outages while preventing reverse power flow (RPF). The system intelligently reconfigures the network at the feeder level and optimizes distributed energy resources (DERs). Operating in two phases, it first continuously evaluates how to form SAPS islands by allocating DERs to critical loads under current constraints, proactively preparing for outages and mitigating RPF risks. Once SAPS islands are formed, the system monitors and reassesses their configuration to improve service to critical loads. This adaptive and proactive approach enhances reliability and resilience in power distribution, ensuring essential operations are maintained even during disruptions.

The invention discloses an infrastructure emergency repair cooperative scheduling method for cascade time uncertainty, and relates to the technical field of emergency management and intelligent scheduling optimization. The method comprises the following steps: 1, carrying out structured modeling on a disaster infrastructure system, a failure unit, technicians and a road network topology; step 2, constructing a task allocation and collaborative path integrated optimization model with planning recovery time efficiency maximization as a target; 3, based on the mean variance and the normal quantile, establishing a probability constraint of step-by-step transmission and accumulation of the uncertainty of the repair duration and the arrival time along the path; step 4, realizing error controllable linearization of the square root relationship by adopting segmented secant lines; and 5, combining variable neighborhood search and integer programming local optimization to quickly solve and output an executable scheme. The method provided by the invention can effectively improve the timeliness of post-disaster first-aid repair and the robust performability of the scheme, and provides scientific decision support for the emergency first-aid repair of key infrastructures such as electric power, water supply, traffic, communication and the like.

This invention discloses a patrol path decision-making method for unmanned systems in adversarial environments, belonging to the field of autonomous unmanned systems and intelligent decision-making technology. The method includes: acquiring an undirected patrol topology graph of the physical environment to be patrolled; calculating the steady-state distribution of the target based on the value parameters, attack time parameters, and degree of each node; and defining a patrol strategy unpredictability index weighted by intra-strategy entropy and inter-strategy entropy. PSUI To address the uncertainty of strategies in terms of microscopic randomness and macroscopic diversity, a simulated annealing dual-entropy balance optimization algorithm is employed to maximize... PSUI To achieve the goal, a set of state transition matrix strategies is generated under the constraints of topological adjacency, row normalization, and target steady-state distribution. The unmanned system is then controlled to perform spatiotemporally decoupled patrols through random matrix switching and probabilistic transitions within the matrix. This invention unifies patrol strategy evaluation and optimization, significantly improves the unpredictability of patrol paths, and enhances the protection capabilities for critical infrastructure.

The invention discloses a PTP network encryption and key management method and device, equipment, a medium and a product, and relates to the technical field of network security, and the method comprises the steps: obtaining a key pair distribution request, and generating and distributing an asymmetric public and private key pair to a target node based on the key pair distribution request; deploying the target node in the PTP network, and obtaining a data transmission encryption algorithm and a key request of the target node; the data transmission encryption algorithm and the key request comprise a node digital signature set based on an asymmetric public and private key pair; based on the PTP domain where the target node is located, correspondingly generating a data transmission encryption algorithm and a secret key, and distributing the algorithm and the secret key to the target node; wherein the basic nodes in different PTP domains are distributed with different data transmission encryption algorithms and keys, and the keys comprise a key for data communication and a key for PTP authentication. According to the invention, a comprehensive PTP security solution is provided for a high-security demand scene, and the PTP network security and reliability of the key infrastructure are improved.

To protect critical infrastructure from the adverse effects of volcanic ashfall, a natural phenomenon in Japan, we aim to provide a long-life filter system that can be used even during prolonged volcanic ashfall, and to meet the demand for proposals for volcanic ash filter systems. This system will protect major facilities and equipment from atmospheric volcanic ash or fine dust, ensure business continuity in emergencies, and protect defense facilities of national security agencies. The goal is to provide a long-life atmospheric dust filter system that blocks volcanic ash and atmospheric dust, allowing essential equipment such as cooling systems for critical machinery and facilities to always function normally. [Solution] A filter device in which a substantially corrugated filter material is attached to the filter surface, characterized in that the filter material is made of a material that prevents deformation of the attached shape of the filter material by air passing through the filter surface.

The invention discloses a containerized intelligent proxy gateway dynamic management system, method and device and a storage medium, the system integrates an intelligent routing function into a traffic scheduling module to form a decoupling type collaborative architecture of intelligent scheduling, deep analysis and multi-dimensional monitoring archiving, the technical contradiction that high-performance forwarding and deep analysis cannot be achieved at the same time is solved, and the system has a wide application prospect. And a dynamic rendering and script hot replacement mechanism is provided, so that the customization of system behaviors during operation is realized, the problem of rigid mirror image configuration of a static container is solved, enterprise-level resource isolation and process guarding capabilities are provided, the reliability of the system as a key infrastructure is guaranteed, and the system performance is improved. Through the asynchronous bypass monitoring archiving module, comprehensive monitoring of container states, performance indexes and user behaviors is achieved, specific protocols of all applications can be analyzed, user behavior data can be extracted and mapped to a unified interest label system, cross-application user portraits can be generated, and data support can be provided for commercial applications such as precision marketing and content recommendation.

A method for damaging an attacker (26) targeting critical infrastructure is disclosed, comprising the following steps: a) sensor-based monitoring of the critical infrastructure for the detection of attackers (26); b) dispatching a single-use unmanned underwater vehicle (20) equipped with explosives (28) in the direction of the attacker (26) when the attacker (26) has been detected; c) automatically attaching the unmanned underwater vehicle (20) to the attacker (26) when the unmanned underwater vehicle (20) has reached the attacker (26); d) detonating the explosives (28) of the unmanned underwater vehicle (20) once the unmanned underwater vehicle (20) is attached to the attacker (26).

The invention relates to the technical field of navigation and timing, in particular to a GNSS timing data prediction method and related equipment, and the method comprises the steps: obtaining and preprocessing Roland timing data when the GNSS timing data is invalid; and inputting the preprocessed data into a trained single hidden layer extreme learning machine model, calculating a hidden layer output matrix by the model through randomly initializing an input layer weight matrix and an offset vector, and solving an output weight by adopting a regularization least square method. And the model outputs a normalized GNSS timing data prediction value, and a final GNSS timing prediction result is obtained after reverse normalization processing. According to the method, high stability of the Rowland system and efficient computing power of the extreme learning machine are combined, accurate prediction of GNSS timing data is achieved, the method is suitable for positioning, navigation and time service toughness enhancement scenes of key infrastructures, and the continuous operation capacity of the system when GNSS signals are interfered or fail is guaranteed.

The invention belongs to the field of power network security data, particularly relates to a correlation analysis system based on power network security data, and aims at existing common information security systems including an anti-virus system, a firewall, an intrusion detection system, a vulnerability scanning system, a UTM and the like. In order to solve the problems that in the prior art, in the prior art, existing security systems generally can only prevent security threats from a certain specific aspect from blocking and do not have a synergistic effect, the following scheme is provided: the security system comprises a data acquisition layer, a correlation analysis layer and a collaborative defense layer which are connected in sequence; the data acquisition layer comprises a multi-source data access module, a data cleaning module, a data classification module, a data storage module, a feature extraction module and a feature updating module, and through a closed loop of data association-intelligent decision-active defense, the problems of data islands and response lag in traditional power network security are solved; and a technical normal form is provided for key infrastructure protection.

A storm damage response system includes a storm ensemble database that stores ensemble forecast models associated with potential storm paths of a storm across a geographic area and an inventory database that stores inventory data associated with location and characteristics of power-providing equipment in the geographic area. A storm damage model algorithm generates a storm response plan comprising an operational procedure for repairing or maintaining power transmission and distribution electric systems to mitigate storm damage impact based on generating a probabilistic model for each of the ensemble forecast models based on the inventory data and calculating a statistical impact value associated with the probabilistic model based on an aggregate of iterative probabilistic simulations for the respective ensemble forecast model. The storm response plan can be generated based on the relative statistical impact value of the probabilistic model of each of the ensemble forecast models.