343 results about "Security rule" patented technology

The present disclosure describes systems and methods for detecting and preventing data misconfigurations within a security-focused data fabric platform. The system integrates an advanced script migration engine designed to streamline the translation of security rules and scripts across different scripting languages while ensuring alignment with the fabric's unified schema. The method involves receiving inputs from data sources, mapping these inputs to entities of a target schema, monitoring real-time data changes, and simulating impacts on operational dependencies to detect misconfigurations proactively. Leveraging AI-driven mechanisms, including Large Language Models (LLMs), the system dynamically identifies breaking changes in third-party data streams, issues alerts, and provides suggested fixes. The script migration engine further enhances the platform's functionality by automating cross-platform script translations and enabling faster onboarding of security tools. Together, these innovations ensure scalable, accurate, and resilient integration and management of security data across heterogeneous sources, strengthening operational integrity and minimizing security risks.

A system detects changes in model context protocol (“MCP”) processes, and performs a remedial action. A server-sent events (“SSE”) bridge sends a request to an MCP server. A first resource profile is received from the MCP server. This is stored and compared against a second updated version of the resource profile. When a difference is detected, the SSE bridge determines whether to block a resource command from reaching the SSE bridge. The decision is based on comparing the difference to security rules, which can be defined as part of a management profile.

The invention discloses a blasting operation field behavior compliance identification method, system and device and a medium, and relates to the technical field of computers. The method comprises the following steps: acquiring a multi-source video stream and environment monitoring data of a blasting operation site; identifying a current operation stage according to the multi-source video stream; identifying a scene risk factor according to the multi-source video stream and the environment monitoring data; inputting the multi-source video stream into a pre-trained deep learning model, and analyzing identity information, a behavior sequence, an equipment wearing state and position information of each operator; calling a compliance rule set matched with the current operation stage and the scene risk factor in a pre-constructed security rule library; based on the compliance rule set, performing compliance verification on the identity information, the behavior sequence, the equipment wearing state and the position information; and if the violation behavior exists through verification, generating a violation judgment result containing the violation type, the risk level, the associated personnel and the spatial region identifier. According to the invention, intelligent monitoring and violation identification of the whole process of blasting operation can be realized.

A resource attack path detector parses IAM policies to identify entities and permissions relating the entities. The resource attack path detector builds a directed graph that represents the entities, which includes principals and resources, with nodes and relates nodes based on permissions. The resource attack path detector indicates properties of the nodes and edges in the graph based on information about the entities. The attack path detector assigns weights to the nodes and edges based on the properties of the nodes and edges. After the graph is complete, the attack path detector can analyze the graph to reveal attack paths. The resource attack path detector scores attack paths and then ranks and / or filters the attack paths based on the scoring. In addition, the attack path detector can extract patterns from attack paths and create security rules with the extracted patterns.

Various aspects of the disclosure relate to enforcing dynamically updated network security policies in real-time (or upon an identified update) from multiple organizations and anonymously analyze computing system configuration information uploaded from third-party computing systems. An analysis engine monitors compliance information and compare the compliance information against the security rules and / or requirements for one or more enterprise networks. A visualization providing a network map with a visual representation of each product system service system may include communication links between internal applications and / or computing systems and drill-down capability to identify issues as they are occurring or are predicted to occur. The security escrow system may include a mechanism to automatically enable / disable access between third party networks and one or more enterprise computing systems in real-time based on identified compliance information.

The invention discloses a police affair studying and judging method based on behavior feature recognition, and relates to the technical field of intelligent police affair and behavior modeling, and the method comprises the steps: collecting dynamic behavior data of a target object, and constructing a dynamic behavior portrait map comprising a behavior event, a time node, a spatial position and an interaction object; time sequence modeling is carried out on the map, and individual behavior chain feature vectors are extracted; comparing the current behavior chain with the historical behavior chain and the group mean value model, and calculating the deviation degree; performing cross-regional universality evaluation on the deviation degree through a transfer learning model; if the deviation degree exceeds a preset threshold value, generating early warning information in combination with a regional safety rule and pushing the early warning information to a police research and judgment system; according to the method, accurate identification and hierarchical response of abnormal behaviors can be realized, and the method has high robustness, strong generalization ability and good actual combat adaptability.

A method for managing security rules in a cloud computing environment. The method includes detecting the creation of a new cloud account and obtaining metadata associated with it. The metadata is compared to that of existing cloud accounts to identify a similar existing account. A segmentation policy associated with the identified existing account is then used to automatically configure a new set of security rules for the new cloud account. The method also monitors the security rules of the existing account for changes and propagates those changes to the new cloud account when detected, ensuring synchronized security policies across similar cloud accounts.

The disclosure provides an approach for adaptive computing security. Embodiments include receiving, by a security rule adaptation system, alert information about a plurality of alerts generated based on one or more security rules. Embodiments include receiving, by the security rule adaptation system, user feedback with respect to a subset of the plurality of alerts. Embodiments include modifying, by the security rule adaptation system, a security rule based on the alert information and the user feedback to produce an adapted security rule. Embodiments include providing, by the security rule adaptation system, the adapted security rule to a security component, wherein the security component uses the adapted rule to detect computing security threats.

The invention includes a UI that allows addition of functional placeholders to an AI pipeline, and a rules engine that chooses pipeline objects during or before AI pipeline execution. The rules engine can select pipeline objects meeting the same function type and object rules of the functional placeholder. The rules engine also applies customizable security rules and artificial intelligence (AI) model routing to queries. Remedial actions are taken when the rule evaluation exceeds a threshold. The remedial actions include transforming the query by replacing sensitive information with a reversible placeholder. The actual result can be modified by reversing the placeholder and redacting additional sensitive information. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.

The invention discloses a VR interaction method and system for virtual disassembly and assembly teaching of a three-electric system of a new energy automobile. The invention relates to the technical field of VR teaching. And loading a 3D model and a scene configuration file, updating a virtual scene in real time, constructing a scene topological structure, recording a component relationship, and initializing security rules and process configuration. Loading the 3D model and the scene configuration file by using an asynchronous loading technology based on a GPU (Graphics Processing Unit); updating the virtual scene in real time and prompting information; the father-son hierarchical relation of the components is recorded through the tree-shaped data structure, the disassembly and assembly process is controlled based on the constructed behavior tree, and it is ensured that students operate according to the correct sequence and steps. Safety rules and flow configuration are built in, for example, high-voltage electricity must be cut off firstly when the battery is disassembled and assembled, and safety accidents caused by misoperation are effectively avoided. And when the operation of the student is illegal, the state machine is triggered to roll back to the latest legal state, so that the student is helped to correct errors in time, and the smooth operation of the disassembly and assembly process is ensured.

The invention provides a payment service security protection method and system based on a block chain, and the method comprises the steps: determining a deployment strategy for a block chain node based on an overall payment service range, building a distributed node based on the deployment strategy, achieving the building of the distributed node for a payment service, guaranteeing the safe and stable completion of the payment service, and improving the safety of the payment service. Based on security rules, a consensus node meeting the current payment service is selected from distributed nodes, a proper node is selected for the current payment service for transaction, transaction security is ensured, transaction security verification is performed on the current payment service based on the consensus node, the current payment service is completed after verification is passed, and the current payment service is completed after transaction security verification is passed. According to the method, malicious attacks are prevented, payment data generated after the current payment service is completed are fragmented, encrypted and stored in the storage node, the safety of the payment data is ensured, and the payment data are effectively prevented from being maliciously modified or deleted through fragmented storage.

Techniques are provided for securing software development cycles with artificial intelligence customization. Operations may include identifying a software generation task; providing the software generation task to a language model; identifying, from the language model, a plurality of queries associated with specific attributes of the software generation task; creating, based on the software generation task and responses to the plurality of queries, a reconstructed software generation task; assigning, based on a machine learning model, one or more security labels to the reconstructed software generation task; determining one or more prioritization scores for one or more security rules based on the one or more security labels; and generating, based on the one or more prioritization scores, at least one security action for the reconstructed software generation task.

Disclosed herein are systems and method for detecting malicious activity on a web server. A method may include: retrieving a first backup and a second backup of a web server from a backup archive that stores a plurality of backups of the web server, wherein the first backup was generated at a first time and the second backup was generated at a second time; detecting at least one change between the first backup and the second backup; determining whether the at least one change is associated with malicious activity based on a plurality of security rules and a plurality of machine learning models and a severity of the malicious activity; and in response to determining that the severity is greater than a threshold severity, executing a rollback function of the web server to a backup that does not include the malicious activity.

The invention provides a security analysis method in a software research and development process. The security analysis method can be applied to the technical field of big data and the technical field of artificial intelligence. The method comprises the following steps: embedding a security analysis plug-in in an integrated development environment; scanning codes written by a user in the integrated development environment in real time through safety rule information preset in the safety analysis plug-in and a heuristic algorithm; identifying a target security problem in response to the security analysis plug-in, marking the target security problem by using the security analysis plug-in, and providing repair suggestion information; and responding to the condition that the user modifies the code based on the repair suggestion information, and utilizing the security analysis plug-in to re-scan the modified code to verify the modification effect.

The invention relates to the technical field of electric power, in particular to an unmanned aerial vehicle route automatic planning method and system for a complex space of a transformer substation. Analyzing at least one inspection task instruction input by a user to obtain a task type corresponding to each inspection task instruction and a data acquisition requirement corresponding to the task type; according to a preset three-dimensional semantic map of the transformer substation and the data acquisition requirement of each inspection task instruction, a flight route of at least one unmanned aerial vehicle is automatically generated by adopting a path planning algorithm, and the flight route comprises a waypoint sequence, a flight height, a shooting angle and a holder action instruction; the three-dimensional semantic map comprises no-fly zone information in the transformer substation, a plurality of devices, the position of each device, the device type, the electrification attribute and the safety rule label. And controlling the corresponding unmanned aerial vehicle to inspect target equipment in the transformer substation according to the flight route of each unmanned aerial vehicle. And large-scale and standardized inspection operation is realized.

The invention relates to a public multi-mode cloud network resource software security enhancement method based on neural symbol fusion reasoning, which comprises the following steps: an intermediate representation generation stage: generating and optimizing an intermediate representation of a program through a fine-tuned large language model, and establishing semantic mapping from a source code to a structured logic representation; in the symbol language conversion stage, the intermediate representation generated by the large language model is converted into a domain-specific language fact set which can be recognized in the symbol logic reasoning stage, and formal and logic expression of program semantics is achieved; and a symbol logic reasoning stage: matching the fact set with the rule base through a symbol logic reasoning engine, performing detection and verification according to the safety rule, generating a structured report, and feeding back a result for optimization. The method is suitable for security enhancement of various core software systems in a public cloud network multi-modal network environment, high-precision security analysis is carried out on cross-modal and cross-subsystem fragmented codes in a compiling-free environment, and verifiable technical support is provided for public cloud network security control.

The invention provides a rules engine that applies customizable security rules and artificial intelligence (AI) model routing to queries. The rules are evaluated with respect to the query, an anticipate result of the query, and the actual result. Remedial actions are taken when the rule evaluation exceeds a threshold. The remedial actions include transforming the query by replacing sensitive information with a reversible placeholder. The actual result can be modified by reversing the placeholder and redacting additional sensitive information. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.

The invention discloses a dynamic configuration vehicle-mounted intention system and control method based on a large language model, and the method comprises the steps: abstracting a vehicle sensor and a controller into attributes through a vehicle-end data plane module, and obtaining a vehicle attribute list and a vehicle control attribute list; the vehicle end intention system module receives a natural language vehicle control instruction of a user, performs semantic analysis by using a large language model, and determines a to-be-executed intention through intention matching; returning an intention configuration and control instruction of the to-be-executed intention; according to the safety rule attribute list of the to-be-executed intention, the real-time state of the vehicle is obtained through the vehicle attribute list, and whether the ODC rule is met or not is judged through the large language model; if yes, based on intention configuration, semantic analysis is conducted on the control instruction according to execution guidance of the intention to be executed, a calling instruction of a vehicle-mounted control tool is generated in combination with the vehicle control attribute list, and a vehicle-mounted data plane module interface is called to complete vehicle control operation. The development difficulty can be reduced, the development period is shortened, the flexibility is improved, and the user experience is improved.

A cybersecurity rule conversion system (“conversion system”) disclosed herein converts IPS / IDS rules from existing formats to target formats in a pipeline that ensures quality of resulting converted rules. First, the conversion system identifies and validates existing rule formats. For rules with validated existing formats, the conversion system maps tokens in the existing format to tokens in the target format and populates a converted rule in the target format. The conversion system then evaluates and tunes patterns in the converted rules for matching range and length. Finally, the conversion system evaluates the converted rules for performance on traffic logs and for quality of patterns and matching ranges of patterns therein, and exports the converted rules to users with indications of performance deficiencies.

A system detects changes in model context protocol (“MCP”) processes, and performs a remedial action. A server-sent events (“SSE”) bridge sends a request to an MCP server. A first list of resource profiles, including tools and instructions, is received from the MCP server. This is stored and compared against a later list of tools and instructions. When a difference is detected, a user interface can display the difference to an administrator. Security rules cause the SSE bridge to be blocked from sending commands to the impacts tool or MCP server until approved by the administrator.

The invention discloses a safety production intelligent monitoring method and device, and the method comprises the steps: employing a safety production large model, combining a video and operation type definition of a safety production operation process, and recognizing the operation type of safety production; obtaining a safety rule corresponding to the identified operation type, wherein the safety rule is obtained by using a safety production large model; obtaining environmental data by using a large safety production model and combining the operation type and the corresponding safety rules and environmental parameters; obtaining a risk behavior analysis result by using a multi-modal large model in combination with the operation type, the corresponding safety rule, the environment data and the video of the safety production operation process; and obtaining risk behavior suggestion measures by using a safety production large model and combining the risk behavior analysis result. According to the invention, the risk behavior can be identified efficiently and accurately, and suggested measures can be given.

A gateway securely executes model context protocol (“MCP”) processes for artificial intelligence (“AI”) agents by creating nano sandboxes in which the MCP processes execute. A server-sent events (“SSE”) bridge identifies a request to initialize an SSE channel with an MCP server, the request being sent from an MCP client that generates MCP-compliant commands in association with an AI agent. The SSE bridge instantiates a nano sandbox based on a definition extracted from the request, creating a custom and dynamic isolated execution environment. An MCP process is launched within the nano sandbox. The SSE bridge returns a session identifier, and receives a command and the session identifier. The SSE bridge authorizes the command and response by applying security rules, blocking either for non-compliance.

The invention relates to the technical field of railway engineering construction safety, in particular to a railway engineering construction potential safety hazard intelligent identification and classification system and method. The system comprises a multi-source heterogeneous data acquisition module, a 5M1E hidden danger feature deconstruction module, a security rule knowledge base module, a risk reasoning and early warning module, a security tag portrait module and a PDCA-SDCA dual-cycle management module. According to the method, a construction site can be comprehensively perceived through multi-modal data, hidden danger characteristics are deeply analyzed based on six dimensions, namely Man, Machine, Material, Method, Envirant and Measurement, of 5MIE, risk reasoning and early warning under a complex scene are realized by using a graph neural network, hidden danger rectification closed loop and knowledge base continuous optimization are realized through double-cycle management, and the method and the system have the advantages that the construction site can be comprehensively perceived, so that the construction efficiency is improved. And the intelligent level and long-term learning ability of railway engineering construction safety management are obviously improved.

An apparatus, having a server and processor, is configured to receive a first set of security rules applicable to a set of users or a set of files for a first period of time. The first set of security rules are executable in an order of priority. The processor receives an interim security policy that is different from the first set of security rules. The interim security policy is applicable to a subset of the set of users for a second period of time that is less than the first period, or a subset of the set of files for a second period of time that is less than the first period. The processor determines, in the first set of security rules, an insertion point among the order of priority. The processor executes, at the insertion point and in the first set of security rules, the interim security policy.

The invention discloses a vehicle-mounted system modular framework and a collaboration method thereof, the vehicle-mounted system modular framework comprises an agent module cluster, and the agent module cluster comprises an interaction agent and at least one function execution agent; a large language model is deployed in each agent, and all the large language models are coordinated through a standardized interface; the interaction agent is used for receiving a natural language instruction of a user and analyzing the natural language instruction by using a large language model deployed in the interaction agent to generate a task instruction; and the function execution agent is used for receiving the task instruction, analyzing and extracting key parameters by using a large language model deployed in the function execution agent, executing security rule check, and executing a corresponding specific task if the task instruction passes the security rule check. According to the method, the rule code quantity can be reduced, the interactive agent realizes cross-agent dynamic task distribution based on the big language model reasoning ability, new function access does not need to modify a bottom layer architecture, only peripheral agents need to be increased or decreased, the development period can be effectively shortened, and the development progress can be accelerated.

The invention discloses a truss robot rehearsal control method based on digital twinning, and the method comprises the following steps: S1, constructing a truss robot digital twinning system, including constructing a truss robot virtual scene and constraint, performing data synchronization and bidirectional communication, and configuring a safety rule base and a data recording / playback interface at a twinning end; s2, rehearsing a task to be executed, generating a reference trajectory and a constraint set, and automatically checking the rehearsing process according to the safety rule base to obtain the reference trajectory and the constraint set which pass the rehearsing; and S3, performing parameterized predictive control and real-time tracking on the rehearsed reference trajectory and the constraint set, and controlling a trajectory tracking error to be within a tolerance range. According to the method, a digital twin system corresponding to the physical world is constructed, Laguerre function parameterized model predictive control is introduced to realize low-dimensional rapid solution, high-precision, reproducible and high-reliability rehearsal control of the truss robot is realized, and the field debugging and operation and maintenance cost is remarkably reduced.

According to the network threat real-time perception and collaborative protection method, device and system and the electronic medium, the network behavior characteristics are extracted to form the industrial control system behavior model, the network behavior mode of the industrial system during normal operation can be deeply known, and therefore abnormity can be recognized more accurately. In the process of generating the security rules, multi-source information including security policies, alarm logs, network behavior characteristics and the like is integrated, so that the security rules are more suitable for the actual situation of the industrial control network, and the risk of misjudgment is reduced. And in the aspect of describing the security threat portrait, not only can the basic threat information be analyzed, but also the risk level can be dynamically evaluated and the propagation path can be determined, so that a comprehensive and accurate basis is provided for subsequent targeted processing. And the linkage disposal process realizes dynamic adjustment of countermeasures, division of linkage elements and setting of linkage conditions and disposal processes according to threat levels and paths, can quickly isolate network security threat sources and block propagation paths of the network security threat sources through cooperative work of protection, recovery and alarm sub-strategies, and carries out timely emergency recovery on a system influenced by threats, so that the safety of the system is improved. The adverse effect of network threats on the industrial control system is reduced to the maximum extent, and the continuity, stability and safety of industrial production are guaranteed.

Various aspects of the disclosure relate to enforcing dynamically updated network security policies in real-time (or upon an identified update) from multiple organizations and anonymously analyze computing system configuration information uploaded from third-party computing systems. An analysis engine monitors compliance information and compare the compliance information against the security rules and / or requirements for one or more enterprise networks. A visualization providing a network map with a visual representation of each product system service system may include communication links between internal applications and / or computing systems and drill-down capability to identify issues as they are occurring or are predicted to occur. The security escrow system may include a mechanism to automatically enable / disable access between third party networks and one or more enterprise computing systems in real-time based on identified compliance information.

The invention relates to the technical field of virtual reality simulation, in particular to a virtual reality immersive practical training content generation method, which comprises the following steps of: constructing a domain knowledge graph: extracting atomic knowledge elements representing operation actions, equipment entities, state parameters and safety rules from a standard document and a case library of a target training domain, establishing a multi-dimensional logic relationship including a causal relationship, a time sequence relationship, a hierarchical relationship and a probability relationship among the atomic knowledge elements, and forming and storing a domain knowledge graph; acquiring trainee data in real time; constructing and updating a student state model; a dynamic content decision step; and generating and injecting nonlinear practical training content. By collecting student data in real time and matching the student data with the knowledge graph, the system can provide accurate feedback for the student after each practical training, and help the student find and overcome own knowledge blind spots.

The invention includes a UI that allows addition of functional placeholders to an AI pipeline, and a rules engine that chooses pipeline objects during or before AI pipeline execution. The rules engine can select pipeline objects meeting the same function type and object rules of the functional placeholder. The rules engine also applies customizable security rules and artificial intelligence (AI) model routing to queries. Remedial actions are taken when the rule evaluation exceeds a threshold. The remedial actions include transforming the query by replacing sensitive information with a reversible placeholder. The actual result can be modified by reversing the placeholder and redacting additional sensitive information. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.