527 results about "Cyber Space" patented technology

The invention discloses a visual language navigation method for cross-modal alignment in a dynamic shielding environment, and the method comprises the steps: collecting multi-modal data through a visual sensor, an inertial measurement unit, a laser radar and the like, and carrying out the preprocessing and time synchronization; sensing the dynamic shielding object through a model composed of a convolutional neural network and a long-short-term memory network, and estimating the future change of the dynamic shielding object in combination with a space-time sequence prediction algorithm; a double-branch convolutional neural network and a Transform based on a dynamic attention mechanism are adopted to respectively extract visual and semantic features and fuse the visual and semantic features; on the basis of occlusion prediction, potential occlusion region features are extracted in advance from a time dimension, an occluded image is repaired by using a generative adversarial network and geometric constraints in a space dimension, and cross-modal feature alignment is optimized through an attention mechanism; planning a path by using a hybrid reinforcement learning algorithm based on a deep Q network-space and a fast exploration random tree, and dynamically adjusting according to real-time shielding; according to the method, the accuracy, adaptability and reliability of visual language navigation in a dynamic shielding environment are improved.

The invention provides an electric power monitoring system network space surveying and mapping method and device, and the method comprises the steps: initializing a plurality of agents based on the structural features and surveying and mapping demands of a target electric power monitoring system network, and distributing surveying and mapping tasks covering different network regions for each agent; based on the surveying and mapping task, each agent collects network dynamic information of different areas of the target power monitoring system network, and obtains a global dynamic network topology based on the network dynamic information of the different areas; based on the global dynamic network topology, the threat knowledge base and the attack mode base, analyzing potential safety hazards and potential threats of the target power monitoring system network through a deep learning algorithm, and generating a threat positioning result and a vulnerability repair suggestion; and formulating a linkage defense strategy based on the threat positioning result and the vulnerability repair suggestion in combination with the state data of the plurality of regional networks and a cooperation mechanism of the plurality of agents. According to the invention, the efficiency and accuracy of the power monitoring system to deal with complex network threats are improved.

The invention discloses a network space security situation awareness detection analysis system and method, and the system comprises a heterogeneous network flow feature deep extraction unit which obtains a flow feature vector through a distributed probe array and an adaptive sampling strategy; the edge computing gateway multi-dimensional threat feature mapping unit realizes feature space mapping through a multi-dimensional feature mapping matrix; the dynamic weight multi-head attention feature fusion unit fuses features by using an optimized multi-head attention mechanism; the time-space sequence security situation evolution modeling unit constructs an evolution model in combination with the time sequence and the fusion features; the security threat risk quantitative evaluation unit carries out risk quantification; and the heterogeneous security policy collaborative response unit generates a response instruction. According to the method, flow collection, feature mapping, fusion, modeling, quantification and response operation are sequentially executed based on all units of the system. According to the method, efficient perception and accurate response of the network space security situation are realized through a multi-unit cooperation and innovation model.

The invention discloses a large language model retrieval enhancement generation method oriented to cyberspace security emergency intelligent analysis. The retrieval and generation capability of a large language model in knowledge questions and answers in professional fields is enhanced through a knowledge graph. The method comprises a knowledge base construction module and a knowledge enhancement response generation module. The method mainly comprises the following steps: collecting multi-source data and preprocessing to construct a basic data set; defining an entity type and a relationship type, and performing entity recognition and relationship extraction based on a large language model to construct a knowledge graph; designing a graph-based hierarchical retrieval strategy to perform knowledge retrieval; and generating professional answers through a knowledge enhancement generation framework. According to the method, the accuracy of knowledge questions and answers in the professional field is remarkably improved, the accuracy of the professional field in experimental evaluation reaches 86.7%, the coverage rate of retrieval knowledge reaches 81.2%, and the interpretation quality score is 4.35 score. According to the method, the limitation of a traditional large language model in professional field application is overcome, and good expandability and adaptability are achieved.

The invention discloses a network space security risk intelligent identification method and system. The method comprises the following steps: deploying traffic collection equipment at key network nodes and boundary equipment, and collecting network traffic data and log data; extracting network behavior features and log key fields, and constructing a data feature model; performing vectorization processing based on the data feature model, performing traffic classification by using a deep neural network, and identifying a device type and a device identifier; carrying out noise reduction, redundant information removal and standardization processing on flow data in the data feature model to generate standardized data and storing the standardized data in a vector database; entity extraction is carried out on the standardized data, a knowledge graph is constructed, data distribution is analyzed through a Gaussian mixture model, the probability of abnormal behaviors is calculated in combination with historical data, and attack risks are calculated based on Bayesian decision; and generating an alarm according to the calculated attack risk, tracing the historical record of the attack risk in the knowledge graph, sending a blocking instruction, and identifying the security risk of the cyberspace.

The invention discloses a causal association privacy protection cyberspace surveying and mapping threat detection method and system, and relates to the technical field of information security and cyberspace surveying and mapping. The method specifically comprises the following steps: constructing a causal incidence matrix based on a multi-modal network space surveying and mapping data dynamic fusion method of multi-scale convolution and causal reasoning to realize cross-modal surveying and mapping data feature fusion; according to the dynamic Gaussian noise privacy protection method based on GCN importance evaluation, privacy budget is dynamically allocated by utilizing causal association importance, and fusion feature privacy is protected; according to the self-adaptive pre-training model gradient protection method based on the self-supervised dual disturbance mechanism, the privacy and availability of the model are dynamically protected; according to the network space surveying and mapping threat detection method based on the stacked LSTM model, abnormal modes in multi-scale traffic are learned, and high-precision threat detection is achieved. According to the invention, through causal association modeling and dynamic privacy protection, the threat detection capability in a complex network environment is improved while the data security is guaranteed.

The invention provides a data security method and system fusing an MIN and a unified AI theory, and the method comprises the steps: S1, completing the multi-dimensional identification registration and synchronization of a data space in a multi-identification network MIN based on a data space identity identification DSID and an ontology semantic mapping frame; s2, acquiring a data acquisition record from a multi-identifier network MIN, and constructing an access control atlas in combination with a semantic vector of a data subject identifier DSID-Item; s3, analyzing the access request and verifying the authority, identifying the sensitivity level of the access request, and triggering a privacy enhancement mechanism when the sensitivity level is marked as medium-high-level privacy data; and S4, judging whether the data object reaches a value end point in the life cycle of the data object or not by taking a data space identity (DSID) registered in the multi-identifier network (MIN) as a retrieval anchor point, and if so, executing a verifiable multi-stage destruction operation. According to the invention, a full-life-cycle trusted data security technical scheme can be realized, and intelligent, explainable and efficient security management of data streams in a network space is realized.

The invention discloses a network space map surveying and mapping method and system based on multi-source data fusion, and the method comprises the steps: obtaining a multi-source data set in a unified format based on network flow data, equipment information data and geographic position data; obtaining a network asset entity and an incidence relation graph thereof through entity identification and correlation analysis based on the multi-source data set with the uniform format; obtaining a network space three-dimensional map model through three-dimensional space mapping and visual rendering based on the network asset entity and the association relationship map thereof; based on the network space three-dimensional map model, performing dynamic updating according to the accessed real-time data flow to obtain real-time network space situation data; and obtaining a network security risk assessment result through anomaly detection and threat identification based on the real-time network space situation data. According to the invention, visual display and security situation awareness of the network space are realized, and a brand new decision support tool is provided for network security management.

The invention discloses an urban cross-domain multi-mode network space modeling method, and belongs to the technical field of traffic simulation. In order to solve the problem that urban traffic cross-domain spatial modeling is complex, the method comprises the steps that road, hub and track BIM models are classified, traffic semantics in different BIM objects in the BIM models are recognized, and traffic elements are extracted; geometric element abstraction is carried out, spatial topology connection relations and attribute information of geometric elements are defined, and single-mode traffic network model modeling is carried out; coordinate conversion is carried out, and the relative position coordinates of the single-mode traffic network model are converted into 1984 world geodetic surveying geographic coordinates; a walking traffic network is used as an intermediate network, a walking traffic mode and transfer rules of roads, rails, buses and hubs are used as strategies, network fusion is achieved through a map matching algorithm, and a multi-mode network space is modeled; and carrying out topological structure verification on the obtained multi-mode network space. According to the invention, full-link calculation from design to simulation is realized.

The invention provides a power system network space precision linkage defense control method and device, and belongs to the field of power system management. The method comprises the following steps: generating a network topology structure and a key asset distribution diagram based on real-time dynamic mapping of a power system network space by a space mapping technology, extracting operation state data and potential risk information of network nodes, integrating data sources of different levels of a power system, and performing association analysis by using a deep learning algorithm, so as to obtain a key asset distribution diagram; obtaining a network threat classification result and generating a dynamic defense strategy by integrating the network operation state; wherein the strategy comprises defense path selection, response node scheduling and risk isolation measures; and executing the generated dynamic defense strategy, evaluating the effectiveness of defense measures according to a feedback mechanism, collecting historical defense records, threat features and response effects, and updating a threat knowledge base and a defense rule base based on a data analysis technology. According to the invention, the accuracy and efficiency of network space defense can be improved.

The invention relates to the technical field of network space security, and provides a DoH malicious tunnel traffic detection method and system based on feature fusion, and the method comprises the steps: carrying out the preprocessing of obtained to-be-detected DoH traffic data, carrying out the sequence segmentation processing, obtaining a Token sequence, and extracting features based on a byte sequence feature extractor; statistical features are extracted and standardized, features are extracted through a statistical feature extraction sub-network, and statistical feature vectors are obtained; fusing the byte sequence feature vector with the statistical feature vector to obtain a classification result; the byte sequence feature extractor and the statistical feature extraction sub-network extract features, and training is carried out by adopting a semi-supervised learning framework of dynamic pseudo-tag screening for non-tag training samples. According to the method, multi-modal features are fused, a semi-supervised learning mechanism is introduced, malicious DoH traffic generated by multiple DNS tunnel tools is effectively identified under a limited annotation data set, and the detection precision and generalization ability are improved.

The invention relates to the technical field of cyberspace asset surveying and mapping, in particular to a cyberspace asset dynamic fusion drawing method based on knowledge graph enhancement, which constructs an asset attribute co-reference resolution mechanism based on asset information quality and defines asset information data quality measurement indexes based on real network environment asset information data. And the asset information data quality is comprehensively evaluated, and network semantic co-reference attribute resolution in the multi-source asset information data is guided. A network space asset mapping knowledge domain ontology structure of a six-tuple structure is designed, a network asset entity and asset relation is extracted through asset attribute representation and asset protocol analysis, and the network asset entity and relation are accurately represented in combination with the ontology structure. The extensive resolution domain name asset entity range in the asset information data is identified by using a misreported domain name asset entity alignment algorithm, and effective public main domain assets are extracted to align misreported asset entities, so that network asset fusion drawing noise can be eliminated, and the accuracy of network asset knowledge representation results is improved.

The invention belongs to the technical field of network space cognitive domains, and discloses a text generation method and system for text, image and video multi-modal fusion. According to the method, on the basis of images, videos and text contents published by a user, important features in multiple modes are extracted through a large model, related descriptions are generated, description information sharing is promoted, key information and core concepts are extracted to achieve deep integration among different modes, and finally an accurate text conforming to the context is generated on the basis of the large model. According to the method, multi-modal fusion is realized, clear and accurate visual information is provided, the problem of single text generation content angle is solved, and the problem of expression fuzziness in text generation is reduced.

The invention belongs to the technical field of cyberspace surveying and mapping, and particularly relates to a visual position identification method. According to the method, a trained global feature extraction network is utilized to perform feature extraction on a to-be-queried image and each reference image in a reference library to obtain respective global descriptors, and the similarity between the to-be-queried image and each reference image is calculated according to the respective global descriptors. Identifying the position of the to-be-queried image according to the geographic position of the reference image with the highest similarity; wherein the global feature extraction network firstly extracts features of different scales of an input image, and fuses the features of different scales to obtain a fused feature; carrying out feature enhancement and redundant noise suppression processing on the fusion features by utilizing relation perception global attention to obtain enhanced features; and finally, carrying out feature coding on the enhanced features by utilizing MixVPR so as to generate global descriptors. According to the method, the effect of integrally improving the robustness and generalization in coping with factors such as image view angle and complex appearance environment change and the like can be achieved.

The invention provides a building identification method and system based on three-dimensional point cloud, and particularly relates to the technical field of computer vision and three-dimensional target identification, and the method comprises the steps: carrying out the undersampling, oversampling and data enhancement preprocessing of inputted building roof point cloud data, and carrying out the T-net network space alignment to output the aligned data. Then, an NEWPCT model is obtained by improving a PCT model, aligned data are input into the NEWPCT model, a point cloud feature matrix is generated through a linear coding layer, query, key and value matrixes are obtained through linear transformation, multi-head attention output is calculated by introducing an offset matrix, intermediate correlation features are obtained after splicing and fusion, and a global feature vector is generated through global maximum pooling; and finally, inputting the vector into a classifier, and outputting a building roof type, and the method solves the technical problems of geometric transformation sensitivity and local feature loss while improving the precision and robustness of three-dimensional point cloud building identification, thereby effectively improving the identification precision and robustness.

The invention discloses a network space security intelligent monitoring and analysis system, and relates to the technical field of network security monitoring and analysis, and the system comprises a multi-source data collection module which collects multi-dimensional data in a full-link manner, and the collection frequency is dynamically adjusted along with a network load; the data preprocessing module cleans the fused data and generates a standardized analysis data set; the AI intelligent risk identification module identifies various safety risks in real time through a mixed deep learning model; the real-time response processing module starts differential processing according to a three-level mechanism; the threat traceability analysis module traces an attack link and generates a report; the security situation visualization module displays the security state in multiple dimensions; the data encryption storage module encrypts and protects data and performs double backup; and the system self-optimization module dynamically optimizes the strategy through incremental learning. The method is accurate in risk identification, timely in response processing, reliable in traceability and evidence storage, and efficient in cross-domain cooperation; terminal protection and third-party access control are enhanced, and network space security and stable service operation are comprehensively guaranteed.

The invention relates to the technical field of power network space optimization management, and particularly discloses a power network space optimization analysis method and system based on a power grid GIS, and the method comprises the steps: firstly enabling a power grid GIS port to firstly match the space coordinates of a high-voltage side bus of a power transmission substation and a power distribution feeder starting node, building an electrical connection topology after verification, and evaluating a capacity coupling factor; dividing feeder segments according to topology, calculating a loss rate and a risk index, and correcting the risk index in combination with a capacity coupling factor, thereby realizing accurate quantification of the feeder state and improving risk assessment scientificity; secondly, feature vectors are constructed according to the GIS center points, the loss rates and the risk indexes of the feeder line sections for clustering, GIS layers with labels are output, feeder line clusters in the same state can be recognized conveniently, and support is provided for regional analysis; and finally, detecting a loop gap based on a layer, analyzing connectivity, identifying a weak link and optimizing feedback, accurately positioning a network short board, and providing a targeted reference for an optimization scheme.

The invention discloses a cyberspace advanced long-term threat (APT) trapping method based on a graph neural network, a storage medium and a cyberspace advanced long-term threat trapping device, and relates to the technical field of computers, and the method comprises the steps: carrying out the dimension reduction of original encrypted traffic data based on a hierarchical clustering algorithm, retaining an abnormal traffic cluster, and removing normal traffic redundant data; designing an E-ResGAT model to perform encrypted semantic modeling, and introducing a residual connection technology to retain original traffic features; and a dynamic trapping-protocol inference closed-loop system is further constructed, and a honeypot environment is utilized to simulate attack sample interaction behaviors and capture traffic. According to the scheme, efficiency balance of encrypted traffic data reduction and semantic reasoning is realized through collaborative optimization of hierarchical clustering and a graph neural network, the capture capability of the model on a hidden attack mode is enhanced in combination with a residual attention mechanism, and a multi-dimensional analysis basis is provided for high-security scenes such as a military private network; and an innovative solution is provided for improving the security defense capability of a military information system.

The invention relates to an IPv6 network space active address and port detection method and device, and belongs to the technical field of network detection. The method comprises the following steps: (1) retrieving network resources; (2) active address detection is divided into a non-seed address scene, a few-seed address scene and a multi-seed address scene, a non-seed address scene generation model, a few-seed address scene generation model and a multi-seed address scene generation model are provided, and candidate addresses are generated respectively; (3) respectively carrying out alias prefix detection and alias removal on active addresses found in a non-seed address scene, a few-seed address scene and a multi-seed address scene, and adding newly found addresses after alias removal into an active IPv6 address library; and (4) predicting active ports of the active IPv6 addresses by using a decision model based on a naive Bayes classifier and utilizing all input features, and completing dynamic feedback scanning through a distributed active detection facility to realize accumulation of the active addresses and ports from nothing to existence and from few to many.

The invention discloses a cyberspace surveying and mapping threat detection method and a cyberspace surveying and mapping threat detection system, and belongs to the technical field of network security. Performing multi-dimensional feature extraction including spatial distribution feature extraction, time distribution feature extraction, protocol interaction feature extraction and load response feature extraction to construct feature vectors, and outputting a prediction feature vector of a next time step according to a plurality of feature vector pairs in a latest time window through a learning model; performing error comparison with the actual feature vector to screen out abnormal features, performing attack intention assessment according to the abnormal features to assess an attack intention comprehensive threat coefficient, and finally assessing a threat risk coefficient in combination with the abnormal features and the attack intention comprehensive threat coefficient. The accuracy of attack intention assessment is improved through the multi-dimensional features, and the assessment accuracy of the threat risk coefficient is improved by combining the abnormal features and the attack intention comprehensive threat coefficient.

The invention discloses a short video information detection method, device and equipment and a storage medium, and relates to the technical field of network space security information detection.The method comprises the following steps that multi-modal information in a target short video is obtained, and multiple modal features corresponding to the modal information are extracted; performing feature alignment on the title features and other modal features to obtain a plurality of key features most related to the title; modeling the consistency among a plurality of key features most related to the title to obtain a first fusion feature representation and a second fusion feature representation; splicing the first fusion feature representation, the second fusion feature representation and the title feature to obtain a multi-modal fusion feature; and classifying the multi-modal fusion features to obtain a detection result of the multi-modal information in the target short video. According to the method, modeling is carried out based on feature alignment and semantic consistency, key information in the short video is better mined and fused, and the accuracy of an information detection result is greatly improved.

The present invention discloses a cyberspace mapping threat detection method and system with causal privacy protection, which relates to the field of information security and cyberspace mapping technology. Specifically, it includes: a dynamic fusion method of multimodal cyberspace mapping data based on multi-scale convolution and causal reasoning, which constructs a causal matrix to realize cross-modal mapping data feature fusion; a dynamic Gaussian noise privacy protection method based on GCN importance evaluation, which dynamically allocates privacy budgets based on causal importance to protect the privacy of fusion features; an adaptive pre-training model gradient protection method based on a self-supervised dual perturbation mechanism, which dynamically protects model privacy and availability; a cyberspace mapping threat detection method based on a stacked LSTM model, which learns abnormal patterns in multi-scale traffic to achieve high-precision threat detection. The present invention improves threat detection capabilities in complex network environments while ensuring data security through causal modeling and dynamic privacy protection.

The invention belongs to the technical field of network security, and particularly relates to a threat intelligence automatic extension method based on network surveying and mapping, which comprises the steps of automatically collecting and preprocessing multi-source threat intelligence by a large model, extracting key network identification information, generating standardized network asset characteristics, and obtaining a multi-dimensional asset characteristic matrix based on a network space surveying and mapping technology. Constructing a network asset fingerprint database, carrying out correlation verification, generating a set of verifiable network asset fingerprints, carrying out feature vectorization on the verifiable network asset fingerprints, carrying out homologous asset analysis through a weighted clustering algorithm, and outputting a threat entity topological graph comprising an IP address cluster, a domain name correlation group and a C2 infrastructure. According to the method, potential threats which are difficult to cover by traditional manual analysis can be automatically identified, an integrated multi-source threat intelligence platform and a network surveying and mapping tool are supported, diversified intelligence formats and feature requirements can be processed, the automation degree is high, threat paths can be rapidly expanded, and response time can be shortened.

The invention discloses a longitudinal federal random forest model training method, system and device based on a Stackelberg game incentive mechanism and a medium, and belongs to the field of cyberspace security, and the model training method completes training of a random forest model through four stages of sample sampling, feature sampling, model training and model fusion. A passive party payment strategy is defined through an active party payment upper limit, then benefits of a decision-making tree model are defined, finally an objective function of an active party is calculated, excitation of participants in a federated learning task is achieved through an excitation mechanism of the two-stage Stackelberg game, and the active party can complete the federated learning task by setting a reasonable excitation strategy and objective. The passive party is stimulated to participate and pay data, so that the overall federal learning effect and the model performance are improved; the system, the equipment and the medium are used for implementing the method. The method is high in safety and fairness, and guarantees the performance of a global model.

The invention discloses a Chinese error correction method and system based on multi-agent collaboration and retrieval enhancement, and belongs to the technical field of network space security. The method comprises the following steps: step 1, constructing a plurality of Chinese error correction expert agents and one Chinese error correction judgment agent based on a large language model and an agent prompt project; step 2, establishing an error correction text knowledge base, converting corpora in the knowledge base into a vector database, and accessing a Chinese error correction expert agent and a judgment agent for information retrieval of the Chinese error correction expert agent and the judgment agent, enhancing context understanding ability and realizing a retrieval enhancement generation function; and step 3, designing a detection prompt, a discrimination prompt and a feedback prompt, constructing a multi-agent closed-loop feedback module, realizing information collaboration, step-by-step reasoning, result verification and iterative optimization of each agent, and completing a Chinese error correction task. According to the method, generalization error correction can be realized under the condition of limited training data, and the adaptability of the model to unknown errors is improved.

The invention provides an efficient task scheduling strategy recommendation method and system for a distributed network surveying and mapping scene, and relates to the crossing field of network space surveying and mapping, network security, distributed scheduling and intelligent optimization recommendation algorithms. Analyzing the task request to extract a task type, a task target network address set, a regional distribution feature, a task execution timeliness requirement and calculation and bandwidth resources required by estimation, and forming a task feature vector; and performing state acquisition and capability evaluation on the distributed surveying and mapping nodes based on task resource requirements defined by the task feature vectors, and performing normalization processing to form node capability vectors. Through multi-source information vectorization and closed-loop optimization, adaptive matching of task scheduling and resource dynamics is realized.

The invention relates to the technical field of network situation awareness, in particular to a network situation intelligent awareness security system based on multi-source data analysis, which accurately acquires a perception threat confidence vector, a service interruption risk vector, an attack chain complete vector and a user behavior deviation vector based on multi-source data fusion and targeted analysis. According to the method, network situation key information can be comprehensively and accurately captured, a reliable basis is provided for subsequent decision making, the comprehensiveness and real-time performance of threat perception are improved, dynamic balance of safety protection, service efficiency and operation and maintenance cost is achieved by relying on a multi-target game mechanism and a network space digital twinning technology, and the safety and reliability of threat perception are improved. A simulation deduction deviation value is introduced as a core index of strategy evaluation, a real network situation is simulated through a digital twin environment, the difference between a strategy execution effect and an expected target is quantified, and in combination with reproduction of an attack scene by the twin environment, deviation value analysis is ensured to be more fit with the real network environment.

The invention discloses an underwater acoustic opportunistic routing protocol method based on directional transmission. A traditional underwater acoustic opportunistic routing protocol adopts an omni-directional transmission mode, the problem of high data packet redundancy rate exists, and the phenomena of bypassing a far path and routing voids are easy to occur in a local sparse region, so that the network throughput is reduced and the energy consumption is increased. Based on the defects, a beam forming technology of a directional transducer is introduced, so that a sending node can directionally align to a target node to forward data, and the network space multiplexing rate is improved. And in combination with the advantage that the transmission distance of the directional transducer is farther under the same transmission power, the occurrence probability of routing voids is reduced, and the transmission delay of the network is reduced. And three factors of depth information, position information and residual energy of the nodes are considered, so that the average life of the network is prolonged. Compared with a traditional routing protocol method based on omnidirectional transmission, the method has the advantage that the performance of an underwater acoustic communication network is greatly improved.

The invention discloses a network space asset real-time perception and risk early warning system based on a neural network, and belongs to the field of network security. In order to solve the problems of poor real-time performance, low accuracy, incapability of processing large-scale data, lack of adaptive ability and the like of a traditional cyberspace asset sensing and early warning method, a three-level collaborative module architecture is constructed and covers an asset sensing layer, a feature processing layer and a risk early warning layer. The asset perception layer collects asset information in real time by using a hybrid detection engine and generates a fingerprint vector; the feature processing layer fuses multi-modal data; and the risk early warning layer evaluates the risk and visualizes the attack path. Meanwhile, a reinforcement learning optimization detection strategy is adopted, a cross-modal alignment loss function is designed, and core algorithms such as space-time causal convolution and a causal attention mechanism are applied. Practice of Guangxi power grid company verifies that the asset state update delay is reduced from 30 minutes to 2 minutes, the novel APT attack detection rate is improved to 92%, the bandwidth occupation is reduced by 40%, and the network security protection level is effectively improved.

The invention discloses a network path detection system, which relates to the technical field of network space surveying and mapping, and realizes balanced cooperation of detection nodes by dynamically adjusting a task allocation strategy through a detection node cooperation module. The path data acquisition module acquires hop point information, time delay data and packet loss rate data in real time by utilizing ICMP, UDP and TCP protocols and a specific flow identifier generation mechanism, and identifies false connection and loop problems in combination with a dynamic load balancing algorithm; the path data processing module standardizes the data into a unified format through a dimensionless technology; the router alias identification module calculates an IPID change rate IPbh based on a monotonicity boundary test technology and a sliding window method, and generates an alias identification result set; the topology analysis and evaluation module calculates and evaluates a network path detection evaluation index Azb, comprehensively analyzes node distribution uniformity, path coverage integrity and topology information effectiveness, and effectively solves the problems of accuracy, integrity and efficiency.