1877 results about "Security system" patented technology

The invention discloses an AI-based smart park multi-source data fusion method and system, and the method comprises the steps: generating a time-space aligned standardized data flow according to environment parameters, energy consumption waveforms, security signals and personnel trajectory data collected by a heterogeneous sensor network; generating a multi-modal fusion feature matrix based on the standardized data stream; according to the multi-modal fusion feature matrix, generating a three-dimensional twinborn body including the equipment state, the people flow density and the energy consumption hot spot in real time; inputting the three-dimensional twin into a multi-target constrained reinforcement learning algorithm, and fusing real-time data and prediction data to generate a Pareto optimal solution set; and based on the Pareto optimal solution set, generating a final instruction set for driving park equipment regulation and control, and triggering collaborative response of a security and protection system and an energy consumption system at the same time. According to the embodiment of the invention, intelligent upgrading of park management can be realized through cross-modal feature extraction, dynamic digital twin modeling and reinforcement learning optimization.

The invention discloses a smart park safety protection system and method based on smart video analysis and multi-mode integration, and relates to the technical field of smart park management. The system comprises a camera, an environment microphone and an access control terminal which are deployed in a park, an edge computing node is used for extracting structured video, audio and access control features, a central server realizes multi-modal space-time alignment through a self-calibration module, a multi-modal fusion module constructs a joint representation vector based on a low-rank tensor algorithm, and the joint representation vector is used for realizing multi-modal space-time alignment. And the security decision module identifies an abnormal event and triggers a response strategy in combination with a classification and anomaly detection model. According to the invention, multi-mode cooperative identification, high-robustness behavior analysis and intelligent response linkage are realized, and the real-time performance, accuracy and intelligent level of a park security system are improved.

The invention discloses a network security verification method and system for a security system, and belongs to the technical field of network security, and the method comprises the steps: obtaining a biological feature recognition request triggered by a user on terminal equipment, synchronously collecting dynamic behavior track data, and generating a behavior feature code; inputting physiological feature data and behavior feature codes in the biological feature recognition request into a feature verification model, when an output biological behavior correlation index is lower than a first preset threshold value, triggering a dynamic verification strategy adjustment instruction, generating a multi-dimensional verification superposition strategy, and executing at least two verification operations in the multi-dimensional verification superposition strategy, the verification result is input into the distributed time synchronization node to generate an encryption token; and when the synchronous matching degree of the encryption token among different nodes exceeds a second preset threshold value, outputting a security verification passing instruction. According to the method, multi-modal feature fusion, dynamic hierarchical verification and time-space encryption technologies are adopted, replay attacks are prevented, an adaptive defense system is constructed, and reliable authentication of a high-security network scene is ensured.

The invention relates to the technical field of communication, in particular to a micro-segmentation-based multi-tenant zero-trust security system, which comprises a tenant network topology sensing module for acquiring network structures and service dependencies of tenants and generating a tenant-level network topology graph and a communication graph; a strategy rule automatic generation module generates a micro-segment access control strategy; the context-aware risk assessment module collects a user identity, an equipment state and a behavior track, and generates an access risk score; the strategy dynamic adjustment and application module updates the access control strategy; and the tenant isolation and zero-trust interaction module deploys a security sandbox to complete trust verification and isolation protection of cross-tenant requests. According to the method, the micro-segmentation strategy is generated by automatically identifying the service dependency boundary, the access control rule is dynamically adjusted based on the context, and refined isolation and risk-driven defense in a multi-tenant environment are realized in combination with the security sandbox and the zero-trust mechanism, so that the security and controllability of the system are remarkably improved.

A computer-implemented method of updating a set of clusters representative of a classification of a text-based dataset into a plurality of different text types for use in a cyber security system is described as part of a classification pipeline. The method comprises receiving text data associated with an entity. The method further comprises generating one or more vector embeddings representative of the text data. The method further comprises using incremental learning to update the set of clusters based on the one or more vector embeddings.

The invention relates to the technical field of information security, in particular to an archive electronic information security system which comprises an archive operation behavior prediction module, an archive data encryption and security storage module, an archive security protection strategy execution module and an archive resource scheduling and security monitoring module. According to the method, through time series data analysis, potential risk behaviors can be recognized in advance, illegal access and abnormal operation can be prevented, losses caused by human errors or malicious behaviors are reduced, data integrity and confidentiality are guaranteed, archive data are stored in blocks, encryption is carried out according to sensitivity and access frequency, and the security of the archive data is improved. Multi-factor identity verification is executed, the flexibility and accuracy of protection measures are enhanced, security vulnerabilities are reduced, priority protection of sensitive archives is ensured through real-time monitoring and resource scheduling optimization, resource waste and risk exposure are avoided, and the security of the sensitive archives is improved. And the overall safety protection capability of archive management in a complex threat environment is improved by collaborative measures.

The technology discloses a computer-implemented policy manager device for a cloud-based security system that manages cloud-based unified functions of packet-level and protocol-level access control and traffic inspection, threat detection and activity contextualization on inspectable and non-inspectable traffic. Packet-level access control inspects packet headers for malformation, protocol-level access control performs deep packet inspection for malicious signatures, threat detection determines whether traffic in an HTTP / S stream as directed to a threat destination, and activity contextualization recognizes whether an activity in an HTTP / S stream accessing a cloud-based application is a compromising activity. Policy manager for a superset of fields specifying security policies across the cloud-based unified functions includes common fields shared by the unified functions, specification receiver handles policy specifications in a common format for values of the common fields, and policy manager is configured to validate, save and distribute policy specifications applicable to respective functions among the cloud-based unified functions.

A cyber security system is adapted to contextualize and visualize cloud architectures featuring ephemeral cloud assets with generation of a cloud asset remediation plan to group alerts for handling based on security team responsibilities.

A cyber security system that protects against cyber threats including a synthetic clone of a voice of a speaker can include several components. A deep learning model is trained to analyze an audio file and produce one or more embeddings of the audio file. One or more AI classifiers are trained to analyze the one or more embeddings of the audio file from the deep learning model to determine whether it is likely that the voice of the speaker engaging with a user is real or the synthetic clone of the voice of the speaker. The voice clone detection bot can be resident on a computing device of the user and can integrate with different sources of audio data on the computing device of the user in order to collect the audio file containing an attempt to synthetically clone the voice of the speaker protected by the cyber security system.

The invention discloses a civil aviation airport security method and system based on multi-source perception and dynamic risk assessment, and the method comprises the steps: building a personnel-article-scene ternary relation model through collecting multi-source data such as video monitoring, face recognition, luggage scanning and sound signals; based on an abnormal matching mode of the model, dynamic risk assessment is carried out by combining real-time people flow density and historical event thermodynamic distribution, and a dynamic risk calibration map is generated; an identification strategy parameter is configured according to the risk level, a self-adaptive strategy set is formed and applied to the intelligent terminal, target identification and behavior tracking are achieved, and a security and protection system is automatically linked when a triggering condition is met; according to the method, the behavior-article-scene ternary relation model is constructed, so that multi-source data collaborative analysis is realized, the limitation of traditional single-mode detection is overcome, the abnormal behavior recognition accuracy in a complex scene is remarkably improved, and the false alarm rate is effectively reduced.

Methods, systems, and apparatus are disclosed for an Artificial Intelligence based cyber security system. An Artificial Intelligence based cyber analyst can make use of a data structure containing multiple tags to assist in creating a consistent, expanding modeling of an ongoing cyber incident. The Artificial Intelligence based cyber analyst can make use of a cyber incident graph database when rendering that incident to an end user. The Artificial Intelligence based cyber analyst can also be used as a mechanism to evaluate the quality of the alerts coming from 3rd parties' security tools both when the system being protected by the cyber security appliance is not actually under attack by a cyber threat as well as during an attack by a cyber threat.

Disclosed is a cloud-based security system implemented in a forward proxy that provides generative artificial intelligence (GenAI) traffic inspection to protect against security and privacy concerns related to GenAI use for protected endpoints. The security system intercepts requests and determines whether those requests are directed to a GenAI application. The security system includes a GenAI request classifier trained to classify prompts submitted to GenAI applications as one of benign, prompt injection attack, or uploaded files. The security system further includes a GenAI response classifier trained to classify responses from GenAI applications as one of normal, leaked system prompt, leaked user uploaded files, or leaked training data. Based on the classification, and optionally other security analysis, the security system may enforce security policies on both the requests and responses that block the traffic, trigger alerts to administrators, and the like to enforce security and privacy protection on bidirectional traffic.

Systems and methods for an Infrastructure as Code (IaC) scanner for infrastructure component security. Various embodiments include steps of receiving one or more files for security scanning; extracting and parsing one or more resources from the one or more files; evaluating one or more policies for each of the one or more resources thereby ensures that underlying infrastructure components are configured securely; and displaying findings and details associated with the evaluating of the one or more resources.

An artificial intelligence (AI) entry management (EM) security system includes a camera, a microphone, a motion detector, a speaker, and a software platform. Geofences are utilized to create boundaries around physical areas, such as locations for package delivery or property boundaries. The EM device and other devices connected to the software platform are configured to monitor the geofences and detect a presence of an activity, an event, an object, or a device. At least one license plate reader is utilized to read license plate characters. Voice recognition and voice identification are used to grant or deny permissions for individuals on a property

Techniques described herein can generate customized, user-based security response recommendations for users of security system(s), such as for security analysts tasked with performing responses to computing security threats. A user-based response recommendation engine can generate the user-based security response recommendations based on incident data associated with security incidents and based on historical user response data. Furthermore, user role inference techniques can optionally be used in conjunction with the user-based response recommendation engine.

The invention discloses a smart park security and protection system and a control method thereof, and relates to the technical field of smart security and protection, the system comprises the following components: a diversified sensing module which integrates a video monitoring sensor, a face recognition sensor and a behavior analysis sensor and constructs an omnibearing sensing network. According to the technical scheme, all-dimensional and multi-angle monitoring and recognition of the park are achieved, the information acquisition capacity of the security and protection system is remarkably improved through the technological innovation, the comprehensiveness and accuracy of data are ensured, the diversified sensing module integrates multiple sensors of video monitoring, infrared detection, face recognition and behavior analysis, each sensor performs own functions and complements each other, and the monitoring and recognition efficiency is improved. According to the system, blind areas and errors possibly generated by a single sensor are effectively eliminated, the multi-angle sensing network enables the system to more accurately capture abnormal conditions in the park, and a solid foundation is provided for subsequent intelligent analysis and early warning.

The invention relates to the technical field of high-speed rail platform safety monitoring, and discloses a high-speed rail platform area intrusion detection and early warning alarm system, which comprises an acoustic and vibration sensor network deployed in a platform area and used for collecting environment signals in real time, the edge calculation unit extracts signal features and compares the signal features with a dynamically maintained environmental rhythm feature baseline to generate a detuning event mark, and the detuning mode analysis device identifies an abnormal mode through space-time correlation and triggers an alarm. According to the method, early perception of subtle anomalies is realized by constructing a multi-modal environment rhythm baseline, active perturbation injection and differential response analysis technologies are combined, the recognition capability of a silent target is remarkably improved, meanwhile, the monitoring continuity under an extreme working condition is ensured by using an elastic baseline adaptive mechanism, and the detection accuracy is improved. And a closed-loop security and protection system from passive sensing to active discrimination is formed.

The invention relates to the technical field of electronic information technology anti-counterfeiting, in particular to an electronic seal verification method based on a quantum true random number and an anti-quantum multi-dimensional dynamic code, which comprises the following steps of: generating a true random number by introducing a quantum random number generator, constructing a quintuple scene space, and fusing multi-dimensional data of a user, a seal, a file, time and a geographic position; an SPHINCS + anti-quantum signature algorithm is adopted, and block chain evidence storage is combined, so that a four-layer nested dynamic security system of quantum random injection, multi-source data binding, anti-quantum encryption and block chain evidence storage is formed. Meanwhile, a whole-process credible system from application, auditing to stamping and verification is constructed, compared with a traditional electronic seal, the quantum-attack-resistant electronic seal has remarkable breakthrough in the aspects of random number safety, quantum attack resistance, data fusion degree, tampering detection sensitivity and the like, and a credible electronic seal solution in the quantum era is provided for finance and other scenes.

The invention, which relates to the technical field of the cloud security system, discloses a monitoring method for a cloud security system comprising a data acquisition and preprocessing system, a threat detection and identification system, a behavior analysis and association analysis system, a risk assessment and early warning system, a response strategy making and execution system, and a monitoring report and log management system. The data acquisition and preprocessing system is used for acquiring network traffic, system logs and user behavior data in real time, and the threat detection and recognition system is used for performing threat detection and recognition on the preprocessed data. The data analysis and machine learning technology extracts significant features related to threats from a large amount of monitoring data, and the features can help a machine learning model better understand and classify potential threats, so that the accuracy and efficiency of threat detection are improved, the system automatically learns and updates threat features, and the threat detection efficiency is improved. And rapid identification and response to novel threats are realized.

The disclosed technology teaches a method for security monitoring in TLS or other certificate-pinned sessions by a cloud-based network security system. An endpoint routing client directs sessions through an inspection proxy by secure tunneling. A secure web gateway buffers encrypted packets in a new session with a cloud-based resource, detects a connection access request from a certificate-pinned application, requests and receives key extraction, and forwards keys to the security system. Traffic is buffered for decryption and forwarded without modification until the keys are available. The keys are applied to allow decryption and re-encryption, on a proxy basis, of traffic between the client and a cloud based system. The inspection proxy applies security policies, even to the TLS or other certificate-pinned session.

The invention discloses an information access security system, equipment and medium based on an iterable trust policy, and relates to the technical field of information, and the system comprises the following steps: reading a configuration file and initializing trust thresholds of different user groups according to historical data; collecting multi-dimensional data of a user in real time, and preprocessing the multi-dimensional data; key features of the user are extracted from the preprocessed data, and a multi-factor trust evaluation model is constructed; based on a multi-factor trust evaluation model, the trust degree of the user is calculated, and a trust threshold value is dynamically adjusted; dynamically adjusting the access authority of the user according to the adjusted trust threshold; recording all operations of trust threshold adjustment and access permission change, and performing reexamination through user feedback; the trust degree of a user is calculated based on a multi-factor trust evaluation model, and a trust threshold value is dynamically adjusted. According to the method, the trust degree of the user can be accurately evaluated in real time, the real-time performance and accuracy of trust evaluation are improved, various security threats can be coped with in time, and the security and reliability of the system are improved.

An artificial intelligence (AI) entry management (EM) security system includes a camera, a microphone, a motion detector, a speaker, and a software platform. Geofences are utilized to create boundaries around physical areas, such as locations for package delivery or property boundaries. The EM device and other devices connected to the software platform are configured to monitor the geofences and detect a presence of an activity, an event, an object, or a device. At least one license plate reader is utilized to read license plate characters. Voice recognition and voice identification are used to grant or deny permissions for individuals on a property.

Provided in the present application are a server, a terminal and a security system. The server comprises an authentication and key management module, a secure multi-party computation engine and a secure-channel management module, wherein the authentication and key management module is used for performing identity verification on a terminal, generating a first private key corresponding to the terminal and a first public key corresponding to the first private key, and generating a first session key on the basis of a second public key of the terminal and the first private key; the secure multi-party computation engine is used for verifying fingerprint information of the terminal and cooperating with a plurality of terminals to perform identity verification on a server; and the secure-channel management module is used for establishing a secure channel with the terminal, encrypting communication data on the basis of the first session key, and using the secure channel to send the encrypted communication data to the terminal. The solution of the present application can effectively solve the security problem of communication between a terminal and a server, and has the advantages of high flexibility and a low cost.

Systems and methods are disclosed relating to the installation of building systems, such as security systems, using machine learning models, such as generative artificial intelligence models. For example, a method can include receiving a prompt indicating inputs from a user that represent components of a building system to be installed. The method can include applying the prompt to a machine learning model comprising at least one transformer, the machine learning model trained using one or more natural language prompts and example system installation data to cause the machine learning model to generate a target layout and installation for the building system. The method can further include presenting the layout for the system using at least one of a display device or an audio output device.

The invention discloses an intelligent operation and maintenance control platform and system for a secondary power system, belonging to field of power system operation and maintenance technology. The platform includes a interval layer, a transmission layer, a station control layer, a system layer, a platform layer, an application layer, and a disaster recovery backup centre. By collecting data from a security system and external information system, and sharing data with other application platforms of a secondary system, problems of omission, repetition, and isolation in current data collection are solved. Balancing the safety of production control area and the needs of advanced application development in information management area, ultimately achieving comprehensive data collection and deep mining, making the production and operation business of the power secondary system more smooth, meeting the professional needs of various professional operators, and achieving the improvement and efficiency of the secondary system.

Methods, systems and apparatuses for sharing video data across tenants of a multi-tenant security system. The methods may include maintaining a first tenant account of a security server and a second tenant account of the security server. The first tenant account is unrelated to the second tenant account. The second tenant account is configured to include partner account privileges for the first tenant account. A request from the second tenant account to access a resource associated with video data included in a resource registry of the first tenant account is accessed. The resource registry is made accessible to the second tenant account based on the partner account privileges. The request may be approved or denied. The video data is stored in a tenant database in association with the second tenant account based on the request being approved. The request may be processed by an image processing device.

The invention belongs to the crossing field of industrial control and edge computing, and particularly relates to an industrial personal computer data security dynamic protection method and system based on AI edge computing. Firstly, industrial control data encryption and standardization preprocessing are achieved through a hardware-level trusted execution environment; secondly, performing real-time anomaly detection on the encrypted data stream based on an AI model, generating a threat score by combining industrial control protocol feature analysis, and forming an edge layer intelligent detection capability; the authentication mode is dynamically switched based on the threat score, and self-adaptive strategy adjustment is realized in a fog layer; then optimizing edge resource allocation according to the threat index and the equipment load, and constructing a cloud elastic scheduling mechanism; and finally, a security system covering data encryption, real-time detection, dynamic protection and resource optimization is formed through grading component cooperation, and the method is suitable for real-time threat detection and self-adaptive response in an industrial internet scene.

The invention provides a security system verification method in an edge computing power secure channel, which comprises the following steps: acquiring computing power, storage resources and network condition information of each node in the edge computing power secure channel to obtain a node capability matrix which is a two-dimensional array, in which rows represent nodes and columns represent performance indexes; according to the node capability matrix, grouping the nodes by adopting a clustering algorithm, and dividing the nodes with similar computing capabilities, storage resources and network conditions into the same group; after each node receives the verification task and the evaluated delay prediction information, a time window of data synchronization is adjusted according to the delay prediction information in task metadata; and after each node completes the verification task, the verification result and the intermediate data are reported to the scheduling node, and the scheduling node compares and verifies the verification results reported by each node group to obtain a final consistent verification result.

Systems and methods for utilizing Large Language Models (LLMs) for improving machine learning models in network and computer security include obtaining tabular data related to an aspect of networking and computer security; converting the tabular data to natural language for each row in the tabular data; inputting the natural language for each row in the tabular data into a Large Language Model (LLM); obtaining an output from the LLM for each row in the tabular data with embedded data therewith; and utilizing the output to train a machine learning model related to the aspect of networking and computer security

The invention provides a remote control method and device for a security system, equipment and a storage medium, and the method comprises the steps: carrying out the feature extraction of security data of a fire alarm host, and obtaining a fire alarm scene vector; a preset linkage strategy library is matched according to the fire alarm scene vector, and a PTZ positioning instruction is obtained; controlling a camera to collect an alarm image based on the PTZ positioning instruction; based on a preset fire alarm analysis model, generating an alarm confirmation report according to the alarm condition image and the fire alarm scene vector; and notifying a plurality of preset user terminals to perform remote interaction according to the alarm confirmation report, and performing start-stop control on the fire-fighting equipment.