33 results about "Threat assessment" patented technology

The application provides a ground target threat evaluation method in an uncertain environment, and the target feature data is repaired by using an improved autoregressive model for the information missing problem of the ground target according to the acquired target situation information; the target threat data sample is generated by using the explainability of the Bayesian network for the problems existing in the current threat evaluation method, and the target threat evaluation model is established by using the nonlinear fitting ability of the BP neural network, so that the final ground target threat level evaluation result is output. The application can efficiently process data missing, solve the problems of threat evaluation sample shortage and model precision, and also integrates the advantages of the model, considers the performance and explainability, is more suitable for the time sequence characteristics and dynamic changes of the ground target data compared with the traditional method, improves the accuracy and robustness of data repair, and provides more complete and reliable basic data for subsequent evaluation.

This invention discloses a data security access control method based on an IoT platform, relating to the field of secure access technology. It includes a dynamic threat assessment step, which determines data risk by analyzing the behavioral sequence of the data parsing process. Specifically, for a specified data type, based on the parsing behavior of historical harmless data samples, a baseline of normal behavior sequence is constructed and its corresponding normal behavior ratio range is determined. Then, the current data to be assessed is parsed, and the real-time behavioral sequence generated during the parsing process is monitored and recorded. This invention overcomes the limitation of traditional access control that only manages who can access the data. By introducing a dynamic threat assessment engine into the access link, it performs in-depth behavioral analysis on the accessed data content itself, constructing a subject-object two-way security system that can effectively defend against data reverse intrusion attacks launched using legitimate data channels.

This invention discloses a method, system, device, and storage medium for assessing the threat of unmanned aerial vehicle (UAV) swarm targets. The method includes: dividing the area surrounding the target into multiple regions; calculating the combat capability threat index and distance threat index of the UAVs in each region, ultimately deriving the threat index for each region. This invention divides the area surrounding the target into multiple regions, then performs a threat assessment on each region separately, deriving the threat index for each region as the basis for threat assessment; and it can also rank the threat indices, providing a reference for subsequent formulation of counter-UAV swarm combat strategies.

The invention discloses an anti-nesting self-adaptive protection method and system for a power transmission line, and relates to the technical field of power transmission line protection. The method comprises the following steps: deploying multiple sensors at key parts of a power transmission line tower, and constructing a sensing network to detect bird activity data flow in real time; calling the nesting threat evaluation dual-channel through an edge calculation unit, carrying out the recognition evaluation of the data flow, and outputting a threat parameter; analyzing the threat parameters based on a bird nesting protection strategy library, and determining a target protection strategy; and according to the strategy parameters, a nesting blocking and expelling linkage device is controlled, and self-adaptive feedback protection is carried out on the tower. The technical problems of short circuit, tripping and other potential safety hazards caused by bird nesting to the safety of the power transmission line are solved, bird activities are accurately detected in real time through a multi-mode sensing network, nesting behavior recognition and threat degree quantification are completed in combination with edge calculation dual-channel evaluation, the risk of short circuit tripping of the line is reduced, and the safety of the power transmission line is improved. And the operation reliability and the intelligent operation and maintenance level of the power transmission line are improved.

The application discloses a kind of in-orbit spacecraft threat degree evaluation methods based on optical detection image information, belong to aerospace technology field, by establishing the mapping relationship of pixel number and space period characteristics, the equivalent strip number of the imaging area of observed spacecraft in critical dimension direction is determined, and the equivalent strip number is used as the optical detection image information quantity of observed spacecraft;Based on the multiple target detection task level of preset, optical detection image information quantity is used as input, based on Johnson criterion, the expected success probability of observation spacecraft in different target detection task level is determined to realize detection task, and the expected success probability is used as the threat degree of observed spacecraft in corresponding target detection task level by observation spacecraft;The threat degree under each target detection task level is compared with the preset threat assessment probability threshold value, and the highest task level not less than the threshold value is mapped as the final threat level.The method can accurately quantify threat degree index.

This application belongs to the field of flight control technology, and specifically relates to an intelligent obstacle avoidance decision-making method for aircraft based on dynamic threat assessment. The method includes: Step 1, determining whether an obstacle exists within the current detection range; if so, proceeding to the threat level assessment stage to determine the threat level of the obstacle; Step 2, constructing an intelligent obstacle avoidance decision-making model and training the model; Step 3, when the distance between the aircraft and the obstacle is less than a preset safety threshold, entering the obstacle avoidance control mode, inputting the states of the aircraft and the obstacle into the intelligent obstacle avoidance decision-making model to obtain actions, and the aircraft executing the actions to achieve obstacle avoidance flight. This intelligent obstacle avoidance decision-making method for aircraft based on dynamic threat assessment can optimize the aircraft's flight trajectory and adapt to different obstacle types, thereby improving the aircraft's flight safety.

This invention relates to the field of intelligent security operation and maintenance technology, specifically to an artificial intelligence network security system based on multimodal large model training. The system includes a server fault diagnosis module, a network attack detection module, an endpoint security monitoring module, a key management optimization module, and a threat analysis feedback module. In this invention, multidimensional data analysis improves the accuracy of fault prediction, reducing business interruptions caused by sudden hardware failures. Based on server anomaly assessment, network access frequency, source, and command characteristics are evaluated to improve attack detection accuracy and reduce the risk of false positives. Endpoint device execution behavior, resource calls, and behavior sequences are extracted to achieve fine-grained security monitoring, enhancing attack tracing capabilities. Key policies are dynamically adjusted to improve security adaptability and reduce policy lag risks. Multi-level data is integrated to calculate the fit between threat behavior and attack, enhancing the precision and response speed of threat assessment and improving overall security situation awareness.

The application discloses a method for aircraft threat assessment based on multi-dimensional situation fusion in a complex electromagnetic environment, divides the whole flight process of the aircraft, selects main threat factors of different flight stages according to actual conditions, selects appropriate threat factors according to different aircraft stages and performs threat calculation, and finally fuses all threat calculation results to perform threat assessment on the aircraft generating threats. The application utilizes the LSTM method to judge the landing point error, introduces a threat intention element, simultaneously considers the influence of radar elements in the terminal flight section, solves the problem that many radar elements cannot directly calculate the threat value by relying on a single radar factor based on the Dempster-Shafer theory, considers the influence of traditional threat factors such as the aircraft-target distance and aircraft threat intention, simultaneously considers the radar factors to meet the threat assessment demand of the aircraft in the complex electromagnetic environment, and makes the application more suitable for actual engineering demand.

This invention discloses an intelligent detection and real-time early warning method and system for foreign object intrusion at railway crossings, belonging to the field of railway traffic operation technology. The method includes joint calibration of a visible light camera, an infrared camera, and a millimeter-wave radar. Based on the calibrated joint sensing devices, visible light images, infrared images, and radar echo signals are collected. An edge computing device detects and locates foreign objects at railway crossings based on the visible light images, infrared images, and radar echo signals. The edge computing device then evaluates the hazard level of the foreign object and issues an early warning based on the detection results. This invention achieves accurate identification and segmentation of foreign objects in complex scenarios through multi-sensor collaboration and an improved YOLO v8 network, significantly improving detection efficiency and response speed. Based on a multi-dimensional hazard scoring model, it achieves quantitative threat assessment and graded early warning, triggering alarms only for high-risk targets, avoiding resource waste, and significantly improving the intelligent level of railway safety management.

The present disclosure provides a method and device for determining address mapping relationship and a storage medium, wherein the method for determining address mapping relationship comprises: under a preset condition, obtaining a mapping relationship table of a preset address and a DRAM physical address; and analyzing a value of a bit address in the DRAM physical address according to a first preset rule to determine an attribute of each bit address in the DRAM physical address, the attribute being used to represent an address domain of the DRAM physical address. By analyzing the value of the bit address in the DRAM physical address, the address domain corresponding to each bit address in the DRAM physical address is determined, the composition of the DRAM physical address can be parsed, and the debugging, modification and analysis of the related problems of the DRAM physical address are facilitated, thereby improving the accuracy and efficiency of the threat assessment of the DRAM physical address vulnerability.

The application provides a distributed multi-unmanned aerial vehicle target threat assessment and task allocation method and system, aiming to solve the problems of single point failure, low efficiency and load imbalance in centralized architecture. The method comprises the following steps: a radar acquires target original track information, a threat assessment module calculates a target comprehensive threat value through multi-dimensional weighting of distance, speed, time and type; a distributed striking unit competes for the target through a collaborative auction algorithm, combines the threat value and striking efficiency to bid, determines the attribution through a consensus algorithm and realizes load balancing; a dynamic reallocation mechanism is triggered for unit failure, target loss and radar disconnection scenes. The system corresponds to radar detection, threat assessment, distributed striking unit cluster, dynamic scheduling and shared data modules, supports distributed collaborative decision-making and state synchronization. The application improves the interception priority of high-threat targets and the rationality of task allocation, enhances the robustness and continuous working ability of the system, and is suitable for multi-unmanned aerial vehicle cooperative combat scenes.

The present disclosure provides an anti-physical attack edge AI model dynamic protection method, device and program product, belonging to the technical field of network security. The anti-physical attack edge AI model dynamic protection method of the present disclosure collects input data stream and physical environment parameters; threat assessment is performed on the input data stream based on a pulse neural network to generate a dynamic defense strategy; a time sequence diagram structure is constructed, and abnormal behavior detection is performed on the time sequence diagram structure through a graph neural network; an AI model is trained locally, and the obtained model gradient is uploaded after differential privacy disturbance is applied; a model update package is received, and a neural pseudo-state physically unclonable function and quantum random number are used to perform physical layer verification on the model update package; and the attack level determined according to the abnormal behavior detection result or the threat assessment result is used to dynamically adjust the system power consumption and the response strategy.

This application provides an active phased array radar detection method and system based on SDR. By acquiring historical spectrum data, it uses HMM / particle filtering to predict the next-hop frequency band and generate a pre-interference template. The fast channel transmits directionally in the predicted sub-band according to the template, while the slow channel performs system identification, protocol analysis, DOA and micro-Doppler analysis, and backfills waveforms and beams. For multiple targets, a three-dimensional joint scheduling system of "time slot-frequency band-beam" is constructed, allocating frequencies according to threat level and inherently constraining frequency bands / sectors, power, sidelobes, etc. The system includes an antenna array, SDR, prediction, parallel processing, scheduling, threat assessment, compliance management, and an industrial control computer, achieving low-latency, accurate, and compliant suppression of fast frequency hopping and concurrent targets.

This solution discloses an attacker perception and profiling method, system, medium, and processor based on IP grayscale values ​​and decoy techniques, relating to the field of network security technology. The method includes: real-time monitoring of accessing IPs and calculation of grayscale values ​​based on multi-dimensional features to achieve dynamic risk classification; intelligent matching of multi-level decoy environments and monitoring strategies according to risk levels; guiding traffic into corresponding decoy environments through transparent guidance technology for comprehensive behavioral monitoring and data collection; real-time analysis of behavioral data based on a predefined feature library and judgment model to identify attackers; and constructing multi-dimensional profiles of accumulated attacker behavioral data and conducting threat assessment. This solution achieves closed-loop linkage of risk identification, decoy handling, behavioral analysis, and intelligence production, improving the accuracy of attacker perception, decoy efficiency, and threat intelligence production capabilities, thereby enhancing the overall effectiveness of the proactive defense system.

The application relates to the technical field of network security, and discloses a honeypot compromised host threat evaluation method and system based on an attack chain. The method comprises the following steps: collecting operation instruction types and time stamps of a same attacker on each compromised honeypot node, and generating a multi-node original behavior sequence; mapping the multi-node original behavior sequence to an attack chain stage label and constructing a cross-node behavior rhythm fingerprint matrix; calculating cross-node rhythm dispersion and locating a rhythm convergence trigger node; and screening real network high-risk assets according to the rhythm convergence trigger node and outputting a forward threat intensity score. The application solves the problem that the existing honeypot compromised host threat evaluation method based on an attack chain cannot identify the target directivity of an attacker through cross-node behavior rhythm convergence characteristics, and thus cannot directively convert honeypot threat evaluation results into defense responses of real network high-risk assets.

This invention provides an AI-based network intrusion prevention data processing method and system. It acquires raw traffic data generated during network communication, discretizes and segments the raw traffic data to generate a set of traffic data units, identifies threat features in each traffic data unit, generates a threat assessment vector based on the matching degree of the identified threat features with a preset threat pattern library, calculates the threat feature correlation degree between adjacent traffic data units based on the threat assessment vectors within a continuous time window, constructs a threat evolution trajectory map, inputs it into a preset generative adversarial network model, optimizes the feature boundaries of the threat evolution trajectory map, and generates a threat determination matrix. It then performs protocol semantic parsing, and verifies the threat confidence of the threat determination matrix based on the parsed protocol field correlations, generating network intrusion prevention commands. This invention can improve the targeting and execution efficiency of network intrusion prevention responses.

This invention provides a network threat assessment method, medium, and system based on dynamic Bayesian networks, belonging to the field of network security technology. The invention uses a parameter adaptive learning unit with a sliding time window mechanism and a Bayesian online change point detection algorithm to update the conditional probability table parameters. It inputs the topology and parameters into a threat temporal inference model that integrates a liquid neuron layer and a hypergraph matching layer, receives real-time security event evidence, and outputs the posterior probability distribution of nodes. Based on the posterior probability distribution, it calculates the neuron activation regulation function value and dynamically adjusts the liquid neuron parameters. When the posterior probability of a threat node exceeds a preset threat threshold, it executes an iterative belief propagation algorithm through a hierarchical distributed inference collaboration module to generate an attack path probability graph and output the network security assessment result. This solves the technical problem of not being able to simultaneously and accurately model the causal dependencies and high-order collaborative modes of network threat events.

This patent presents an integrated multi-sensor fusion system designed to detect and assess drone threats in maritime environments, with a focus on offshore facilities. The system utilizes various sensors, including TDOA, sound, video, and GIS data, processed by specialized neural networks. These data are fused using a Multifaceted Neural Network (MFNN) for comprehensive threat evaluation. A Drone Risk Level (DRL) algorithm assigns risk levels based on drone characteristics. The system addresses maritime-specific challenges, incorporates data augmentation and transfer learning, and offers a robust solution for safeguarding economic, environmental, and national security interests in offshore regions.

This application discloses an intelligent detection method, apparatus, device, and medium for document-based advanced persistent threats (APTs), relating to the field of computer technology. It includes: static preprocessing of the target document to extract its document structure features and metadata, and establishing a document object relationship graph based on these features and metadata; constructing an isolation sandbox using virtualization technology, and performing dynamic behavior analysis on the target document based on the sandbox and the document object relationship graph to generate corresponding dynamic analysis results; using a neural network architecture configured with bidirectional gated recurrent units to perform deep modeling of the dynamic analysis results and analyze the semantic features of the resulting behavioral sequences to output threat assessment results; the behavioral sequences include user operation logs, device interaction records, and text behavior trajectories. This improves the accuracy and efficiency of document-based APT detection and reduces the false positive rate.

This invention discloses a system and method for detecting and countering unmanned aerial vehicle (UAV) swarms, belonging to the field of airspace security and UAV defense technology. The system includes a multi-source heterogeneous sensing module, a dynamic confidence fusion module, a target spatiotemporal correlation module, a swarm threat assessment module, a protocol parsing and precise countermeasure module, and an intelligent adaptive communication module. This invention improves perception robustness in complex environments by dynamically adjusting the multi-sensor fusion weights in real-time evaluation of environmental factors; effectively suppresses false alarms through target spatiotemporal feature analysis; achieves accurate swarm segmentation through dual correlation of kinematics and communication protocols; achieves precise takeover countermeasures through protocol parsing, greatly reducing collateral damage; and ensures high system availability through intelligent adaptive switching via closed-loop monitoring of communication links.

The application discloses a kind of multi-state target threat assessment and collaborative management methods for low-altitude economy, with situation base map as global data set, low-altitude target is divided into compliance, transition state and non-compliant three states and implements unified control, when the target broadcast effective RID has held VID, identity trusted merging is realized by double track tracking and merging consistency comparison;Threat assessment adopts the double-channel architecture of data-driven and expert rule parallel, with compliance deviation degree as common input;When generating evasion route for compliance and transition state target, the evasion route is written into flight plan database as temporary equivalent authorized route synchronously, so that threat assessment calculates deviation according to updated reference route, thereby avoiding evasion behavior being misjudged as route deviation. Before executing countermeasure, compliant targets and transition state targets in the influence domain are identified and disposed by linkage effect prediction, evasion instructions are issued to them first and then execution is disposed, to realize differentiated collaborative management.

This invention discloses a big data-based network security threat assessment system, comprising: a multi-source data acquisition module, a big data processing platform module, an intelligent threat assessment engine module, and a service and response module. This invention relates to the field of network security technology. By integrating multi-dimensional data such as network traffic, terminal behavior, and asset status, it breaks down the data silos of traditional security devices, constructs a comprehensive security situation awareness view, significantly reduces detection blind spots, and adopts a batch-stream integrated architecture to collaboratively analyze real-time data and historical baselines. Combined with supervised and unsupervised machine learning models for fusion detection, it can effectively identify known attacks and unknown anomalies, reducing false alarms while improving the detection rate of advanced threats. It innovatively quantifies and integrates multiple factors such as threat signals, asset criticality, and vulnerability exploitability, outputting dynamic and interpretable risk scores, transforming security response from coarse alarm management to precise risk prioritization management.

A system for the cybersecurity of telecommunications networks (100), comprising: a cloud-native zero-trust control plane (1) configured to generate and enforce access policies for telecommunications workloads, network functions and application programming interfaces; an identity, device and context engine (2) configured to determine an identity context for at least one entity that includes a participant, a device, a network function, a service or an administrator; a telecommunications telemetry acquisition and streaming layer (3) configured to acquire multiparameter telemetry data from at least one telecommunications domain which includes a radio access network, a core network, an IP transport network, an edge cloud or an OSS / BSS domain; a feature engineering and normalization layer (4) configured to transform the collected telemetry data into standardized features for analysis; an ML-based engine for anomaly detection and threat assessment (5) configured to detect anomalous behavior and output a threat assessment based on standardized features and identity context; a correlation, threat intelligence, and explainability module (6) configured to correlate the anomalous behavior with the telecommunications topology context and generate an explainable security alert; and a dashboard (7) for automated response, orchestration and compliance monitoring, configured to trigger one or more remediation actions based on the threat assessment and to log the remediation actions for audit purposes, where the system (100) performs a continuous review of the entity and enforces least-privileged access according to a zero-trust framework, while using machine learning to detect cyber anomalies.

The application discloses a kind of unmanned aerial vehicle autonomous obstacle avoidance and dynamic landing method and system based on vision.The method comprises the following steps: collecting environment image, identifying landing platform and obstacle and tracking and positioning landing platform;Based on obstacle information, threat assessment is carried out, and environment state description is generated by encoding vision and threat information;Based on landing platform position, reward information is constructed, and environment state description is input into autonomous decision network, after feature fusion of reward information and environment state description in horizontal control sub-network, horizontal motion decision is output, and vertical control sub-network outputs vertical motion decision;According to motion decision, control instruction is generated, unmanned aerial vehicle is controlled to avoid obstacle and land to landing platform.The application converts vision information into structured state description by threat assessment mechanism, and guides decision process by fusing reward information and state features, realizes autonomous obstacle avoidance and accurate landing in complex environment.

A threat assessment and remediation analysis (TARA) modeling process includes a threat intelligence database, a threat scenario builder and a countermeasure advisor. An item definition is either a vehicle or an electronic control unit (ECU). If a vehicle, its features are extracted and are converted into an input feature vector. If ECU, its parameters are extracted and converted into an input feature vector. The input feature vector is compared to a feature space of the threat intelligence database that includes any number of feature vectors representing potential attack paths of the vehicle or ECU. The feature vectors closest to the input feature vector are matched and these are output as potential threat scenarios. A countermeasure advisor selects an appropriate countermeasure for each matched feature vector using records in the threat intelligence database and outputs each appropriate countermeasure as a new asset. The countermeasures are downloaded to the vehicle during assembly.

A method for collecting and sharing non-actionable information at a first transit location source hub with a second transit location destination hub. In one example, the method includes identifying, a first person in an image captured by a first camera included in a first surveillance system at the first transit location source hub, wherein the first person has a threat assessment. The method also includes, in response to the threat assessment being above 0 and below a first minimum threshold, identifying the second transit location destination hub to which the first person is expected to travel to via a common carrier vehicle, packaging, into a first container, first information identifying the first person and the threat assessment, and sending the first container to a second surveillance system at the second transit location destination hub via a communication network.

The present application relates to the technical field of unmanned aerial vehicle, and especially relates to a method and device for capturing a target unmanned aerial vehicle, equipment and storage medium; image sequence data and multi-source observation data are predicted according to a preset maneuvering tracking filter model to obtain target state estimation and future predicted trajectory; threat assessment is performed on the target state estimation and future predicted trajectory to obtain a threat assessment result; the threat assessment result is analyzed according to a preset disposal condition to obtain a threat analysis result; the target unmanned aerial vehicle is captured according to the threat analysis result, a preset capture envelope condition and a preset capture distance range; after capturing the target unmanned aerial vehicle, detection and shooting data are collected; by constructing an unmanned aerial vehicle security whole-process disposal system, multi-source sensing and collection data are accurately estimated on target state and trajectory through the maneuvering tracking filter model, disposal decisions are output after quantifying threat assessment, and low-altitude security decision efficiency is improved in all directions.

This invention relates to the field of outdoor power management technology and discloses an outdoor power adaptive power allocation and load management system. The system comprises five modules: data monitoring, status analysis, threat assessment, power adjustment, and information structuring. The data monitoring module acquires real-time data on the outdoor power supply's output power and the load's input power; the status analysis module analyzes the load's operating status and generates status signals; the threat assessment module assesses the degree of load anomaly threat based on these signals; the power adjustment module adjusts the power allocation parameters according to the threat level signal; and the information structuring module extracts load element information and generates structured load data units. This system can dynamically monitor power, identify load status, assess abnormal threats, adjust power allocation, and organize data, making it suitable for efficient management of outdoor power supplies in multi-load scenarios.

The application relates to the technical field of Internet of Things security detection, in particular to an Internet of Things security intelligent detection system based on big data, which comprises a security intelligent detection center, a network perception module, a behavior portrait management module, a multi-dimensional abnormality monitoring module, a multi-dimensional threat evaluation module and a detection visual module, the multi-dimensional threat evaluation module is internally provided with a device correlation unit and a time correlation unit; the application constructs a multi-dimensional baseline through normal operation data of devices, simultaneously realizes real-time optimization of dynamic behavior portraits through an incremental updating mechanism, ensures that a detection benchmark is synchronous with a device operation state, improves adaptability to changes in a device operation mode, discriminates abnormalities from three core dimensions of communication, resources and operation, combines correlation analysis between devices and time correlation analysis, accurately identifies complex threats, constructs a multi-dimensional threat level evaluation system, and improves the safety protection pertinence and effectiveness of the Internet of Things network.