42 results about "Data dependency graph" patented technology

The invention relates to the technical field of distributed processing, and provides a data processing method based on a distributed network, and the method comprises the steps: collecting a dynamic feature set and a real-time resource index set of an input data flow in real time; inputting the dynamic feature set and the real-time resource index set into a pre-trained autonomous optimization model, and generating a joint optimized data fragmentation strategy matrix and a task allocation weight vector; splitting the to-be-processed task into a plurality of sub-tasks with an execution order relationship according to the data dependency graph, and dynamically mapping each sub-task to a specified storage fragment of the target node according to the weight vector; in the subtask execution process, each node continuously exchanges execution state data, and dynamically adjusts the task execution path weight of adjacent nodes according to the progress deviation rate and the resource margin in the state data so as to form a closed loop feedback optimization link; and according to the continuously updated real-time resource index set and the progress deviation rate, triggering a fragment migration transaction and a fault recovery assembly line, and carrying out strategy iteration updating.

The invention provides a multi-strategy caching and automatic data generation method and system based on a Node environment, and relates to the technical field of data processing, and the method comprises the following steps: receiving a client request, obtaining a data generation template, constructing a data dependency graph to analyze a field dependency relationship, and determining a field generation sequence by adopting a recursive generation strategy and depth-first search; data validity verification is executed, a data generation thread pool is created by adopting an adaptive load balancing mechanism based on the data dependency graph, and asynchronous processing is realized through double buffer queues, so that the data generation efficiency is improved, the data logic integrity is ensured, and the system resource consumption is reduced.

A platform operation performance evaluation method and system based on collaborative intelligent analysis relates to the field of electrical digital data processing, and the method comprises the following steps: establishing a directed data dependency graph according to a data association relationship, and arranging operation performance data according to a time sequence to form a performance fluctuation sequence; calculating association importance of a monitored object in the directed data dependency graph, analyzing a performance fluctuation sequence, and calculating a propagation consistency factor of performance data exceeding a preset performance threshold and a time closeness factor of associated performance data in the performance fluctuation sequence; calculating a propagation weight value between any two monitoring objects, constructing a performance fluctuation propagation sequence of the monitoring objects based on the propagation weight value, and calculating the abnormal influence degree of the monitoring objects according to the performance fluctuation propagation sequence and the directed data dependency graph, and determining the monitoring object with the abnormal influence degree exceeding a preset influence degree threshold value as a target abnormal object. By implementing the method, the accuracy of evaluating the operation performance abnormity of the platform can be improved.

The invention relates to a cross-language software vulnerability detection method and device, and the method comprises the steps: carrying out the analysis of a Joern static analysis pair, carrying out the integration and semantic enhancement of an abstract syntax tree, a control flow graph and a data dependence graph, and obtaining a cross-warehouse heterogeneous code graph; obtaining cross-language intermediate representation based on a compiler framework; after the cross-language intermediate representation and the cross-warehouse heterogeneous code graph are modeled, weighted fusion is carried out through a gated cross attention mechanism, and a multi-modal data set is obtained; carrying out migration training on the multi-modal cross-language vulnerability detection model, and carrying out vulnerability detection on cross-language software to obtain a detection result; through multi-modal data fusion and modeling, in combination with cross-language intermediate representation and a cross-warehouse heterogeneous code graph, the defects of a traditional method in the aspects of cross-language generalization ability and context reasoning ability are effectively overcome; the method has the advantages that the generalization ability of cross-language vulnerability detection is improved, the false alarm rate and the missing report rate are reduced, and the comprehensive utilization effect of global structure information is enhanced.

The invention provides an automatic vulnerability detection method based on an intermediate pseudo code and a graph neural network. The method comprises the following steps: converting an assembly code of a binary program into a pseudo code; identifying a sensitive function; constructing a data dependency graph (PDG), and extracting a data dependency sub-graph by taking the key function as a central node; generating a graph semantic embedding vector by combining a pre-training model CodeT5-small and a graph attention network (GAT), and generating a graph structure embedding vector by using a node2vec algorithm; training is carried out through a double-branch feature analysis model based on a graph convolutional neural network (GCN) and a classification model of a multi-layer perceptron (MLP); and detecting vulnerabilities by using the trained model. According to the method, the sensitive function vulnerability of the binary program can be distinguished under the condition that source codes do not exist, the accuracy and efficiency of automatic vulnerability mining are remarkably improved, the limitation of a traditional automatic vulnerability mining method is effectively solved, and the method has important practical application value.

The invention relates to the field of code vulnerability detection, in particular to a binary vulnerability retrieval and positioning method and system based on high-dimensional vulnerability characterization, and the method comprises the steps: firstly inputting a binary file, analyzing the structural information of the binary file, and analyzing a machine instruction; extracting binary file function information, generating a cross-platform basic block IR instruction sequence, constructing a control flow graph, and constructing a data dependency graph; splicing the program dependency graph feature vector and the basic block IR instruction sequence feature vector, and performing multi-modal feature fusion to generate a comprehensive high-dimensional vulnerability representation vector; and finally, inputting the high-dimensional vulnerability representation vector into an index for searching to obtain a plurality of nearest neighbor indexes and distances, converting the distances into similarity, filtering according to a similarity threshold to obtain a most similar vulnerability vector, and obtaining vulnerability information and vulnerability positions corresponding to the vulnerability vector. The problems that traditional binary vulnerability detection cross-platform and compiler optimization detection fails and the result reliability is low are solved.

The invention discloses a cross-platform binary code block hierarchical semantic similarity detection method and a related device, and belongs to the field of code vulnerability detection.The cross-platform binary code block hierarchical semantic similarity detection method comprises the following steps that structural information of a binary file is analyzed, a machine instruction is analyzed through a hybrid disassembling engine, a basic block is extracted from the structural information of the binary file, and the basic block is extracted from the binary file; constructing a control flow graph, a data dependency graph and a function call graph; extracting instruction level semantics, basic block level semantics and function level semantics from the instruction sequence, the basic block, the control flow graph, the data dependency graph and the function call graph; according to the instruction-level semantics, the basic-block-level semantics and the function-level semantics, constructing instruction-level features, basic-block-level features and function-level features; and carrying out hierarchical similarity detection on the instruction-level features, the basic block-level features and the function-level features. The method and the related device can solve the problems of low detection efficiency and poor accuracy when a traditional vulnerability detection method is used for detecting vulnerabilities.

The invention relates to the field of code vulnerability detection, in particular to a vulnerability detection method and system based on code block features. The method comprises the steps that firstly, a binary file is analyzed, and a machine instruction is analyzed through a hybrid disassembling engine; secondly, performing static linear scanning on the extracted binary file instruction, constructing a control flow graph, dividing basic blocks, extracting an operation code sequence in the basic blocks, and constructing a cross-basic-block data dependency graph and a system calling sequence called by the basic blocks; secondly, using an LSH algorithm to realize rapid filtering, performing similarity comparison on extracted basic block features and vulnerability feature library features, realizing coarse-grained matching, and screening out a matching candidate set; feature fusion is carried out on the candidate set basic block operation code sequence, the basic block data dependency graph and the system calling sequence called by the basic block, GNN fine-grained matching is carried out on the fused features, and vulnerability detection is carried out. The problems that traditional binary vulnerability detection is poor in adaptability and low in result reliability are solved.

The invention provides a data persistence defect detection method and device, electronic equipment and a storage medium, and relates to the technical field of database detection.The method comprises the steps that context analysis is conducted on database source codes, and at least one fault injection point in the database source codes is determined; random faults are injected into the fault injection points; determining a theoretical recovery data graph corresponding to the data dependency graph on the basis of a database recovery log under the condition of random fault triggering during database operation; the data dependency graph is used for representing a metadata state of the database before the random fault is triggered; and based on the actual recovery data graph and the theoretical recovery data graph corresponding to the database, determining a data persistence defect detection result corresponding to the database. According to the method, the random fault can be accurately injected into the fault injection point obtained based on context analysis, so that the fault triggering is efficiently simulated when the database runs, and the data persistence defect corresponding to the database is accurately identified.

The invention relates to the field of intelligent analysis, and particularly discloses a binary code-oriented static taint analysis system and method, which introduces an on-demand micro-symbol execution mechanism on the basis of basic data flow analysis through binary lifting and control flow diagram construction, that is, aiming at memory ambiguous nodes in a data dependence diagram, a micro-symbol execution mechanism is introduced on the basis of the basic data flow analysis; refined alias analysis is achieved through reverse instruction slicing and constraint solution, then an enhanced data dependency graph is generated, and path-sensitive taint propagation iteration is conducted on the basis to generate a final taint state table. In this way, complex dynamic memory addressing can be effectively recognized, taint chain breakage and misinformation explosion caused by ambiguity ignoring or excessive approximation are avoided, and resource allocation of an analysis engine is remarkably optimized.

The application relates to the technical field of computer network information security, and discloses an intelligent analysis and judgment system for network security vulnerability detection, which comprises the following modules: a state acquisition and marking module, which extracts a microstate feature vector and assigns a binary stain label based on a propagation condition to generate a high-dimensional point cloud set with labels; a semantic topology reconstruction module, which extracts a control flow graph and a data dependency graph, and generates a non-Euclidean distance weight matrix in combination with Euclidean numerical differences; a relative homology calculation module, which divides a trusted subspace and a full-amount topology space according to the non-Euclidean distance weight matrix to generate relative topological invariant data; and a vulnerability analysis and judgment module, which constructs a high-dimensional continuous bar graph, generates a vulnerability triggering alarm signal when a Wasserstein topology distance exceeds a dynamic security threshold value, and generates a topology deformation gradient to be fed back to a fuzzy test engine. The application eliminates normal execution background interference, and realizes high-precision identification of deep vulnerabilities and intelligent guidance of a test direction.

The invention belongs to the field of kernel vulnerability analysis of operating systems, and particularly relates to a kernel memory vulnerability cause diagnosis method based on causal chain analysis. The method comprises the steps of restoring a data dependency graph and a function call tree in a vulnerability triggering process from instruction-level tracking data in the vulnerability triggering process, and restoring a kernel execution state and kernel behavior semantics in combination with function modeling; analyzing the kernel execution state, and identifying an abnormal execution behavior in the kernel memory vulnerability in combination with the abnormal model of the kernel memory vulnerability; based on causal chain analysis of dynamic programming, causes of vulnerabilities are analyzed and obtained in a series of kernel execution abnormal behaviors. According to the method, the kernel execution state and behavior semantics are restored, the kernel abnormal behavior state is identified, and the causal chain of the abnormal behavior is analyzed, so that the cause of the kernel memory vulnerability can be effectively diagnosed, and the subsequent vulnerability repair work is promoted.

This invention provides a multi-strategy caching and automatic data generation method and system based on a Node environment, relating to the field of data processing technology. The method includes receiving client requests, obtaining data generation templates, constructing a data dependency graph to analyze field dependencies, using a recursive generation strategy and depth-first search to determine the field generation order, performing data validity verification, creating a data generation thread pool based on the data dependency graph using an adaptive load balancing mechanism, and implementing asynchronous processing through a double-buffered queue. This improves data generation efficiency, ensures data logical integrity, and reduces system resource consumption.

This invention provides a method, system, device, and storage medium for smart contract vulnerability detection, belonging to the fields of blockchain security and artificial intelligence technology. It includes extracting an abstract syntax tree, control flow graph, and data dependency graph from the original smart contract code; obtaining a composite graph object based on the abstract syntax tree, control flow graph, and data dependency graph; processing the composite graph object based on node semantics and function call relationships to obtain a hierarchical heterogeneous graph; constructing a heterogeneous graph Transformer network; using a heterogeneous graph Transformer encoder to process the hierarchical heterogeneous graph for encoding node and edge information, obtaining node representation vectors that fuse semantic and structural information; extracting multi-scale features from the node representation vectors; fusing the extracted multi-scale features using learnable weights; and processing the fused multi-scale features using a base classifier cluster to obtain vulnerability type prediction results and confidence levels. This invention achieves full-process optimization from structural modeling and semantic fusion to decision reasoning, significantly improving the accuracy and interpretability of smart contract vulnerability detection.

The application discloses a code search method and system based on multi-structure graph fusion. An early fusion strategy is adopted to fuse three modal information, i.e. abstract syntax tree, data dependency graph and control flow graph, into a functional program graph as the functional input of the code. Since the edges of the multiple graphs are heterogeneous, a meta-path aggregation graph neural network is adopted to distinguish the control flow, data dependency and syntax tree relationship through different edge type processing mechanisms, extract the code features of the graph, and enhance the understanding of the code semantic structure. The meta-path selection is dynamically adjusted through a graph attention mechanism, further weights are allocated to different nodes, key information is highlighted, the timing information in the code segment is captured, the accuracy of the code representation is improved, and the search capability is enhanced.

The invention discloses a clustering method and device for system alarm information, electronic equipment and a computer storage medium. The method comprises the steps that the alarm information is acquired; based on the alarm information, searching for each target data node having an association relationship with the alarm information from a pre-obtained system data dependency graph; establishing an alarm node corresponding to the alarm information in the system data dependency graph, establishing connection between each target data node and the alarm node, forming an alarm state graph added with the alarm node and a connection relationship between the alarm node and each target data node, and providing the alarm state graph to a pre-trained graph representation model; the graph representation model forms an alarm vector corresponding to the alarm information according to the alarm state graph; and clustering alarm vectors formed by the alarm information in the set time window by a predetermined clustering algorithm to obtain a clustering result of the alarm information in the set time window. The method provides a premise for improving the processing efficiency of the alarm information and the positioning efficiency of the fault.

The invention discloses a taint type vulnerability detection method and system oriented to a native PHP application, and belongs to the technical field of vulnerability detection. The method comprises the following steps: generating a control flow diagram of each function in a PHP program source code; the method comprises the following steps of: initializing a function call graph which does not contain any node for a PHP program source code; determining a value range of each variable in the PHP program based on the control flow graph and the initialized function call graph, and carrying out iterative updating on an initial value set analysis result and the initialized function call graph; fusing the final function call graph and the control flow graph to generate an inter-process control flow graph, and executing inter-process arrival constant value analysis based on the inter-process control flow graph and a final value set analysis result to generate a data dependency graph; and propagating the stain information along the data dependence graph, and combining the final value set analysis result to obtain a stain type vulnerability detection result of the PHP program. According to the method, the dynamic value can be accurately deduced, and potential taint type vulnerabilities in the Web application can be found as early as possible.

A platform operation performance evaluation method and system based on collaborative intelligent analysis, relating to the field of electronic digital data processing, is disclosed. The method includes: establishing a directed data dependency graph based on data relationships; arranging operation performance data in chronological order to form a performance fluctuation sequence; calculating the correlation importance of monitored objects in the directed data dependency graph; analyzing the performance fluctuation sequence; calculating the propagation consistency factor of performance data exceeding a preset performance threshold and the temporal tightness factor of related performance data in the performance fluctuation sequence; calculating the propagation weight value between any two monitored objects and constructing a performance fluctuation propagation sequence for the monitored objects based on the propagation weight value; calculating the abnormal impact degree of the monitored objects based on the performance fluctuation propagation sequence and the directed data dependency graph; and identifying monitored objects with an abnormal impact degree exceeding a preset impact degree threshold as target abnormal objects. Implementing this method can improve the accuracy of platform operation performance anomaly evaluation.

The invention relates to the technical field of data processing, and provides an interface case data processing method and device, electronic equipment and a readable storage medium. The method comprises the steps that clustering processing is carried out on to-be-processed flow data based on a preset sorting condition, an ordered interface calling chain is obtained, and the ordered interface calling chain comprises response data and request data; performing full-field value matching processing on the response data and the request data to obtain a data dependency graph; performing parameterization processing on the request data based on the ordered interface call chain and the data dependency graph to obtain target interface case data; and the target interface is verified based on the target interface case data, so that the automation degree of interface testing is improved, the fitting degree of the case data and a real business scene is improved, the verification accuracy, continuity and maintainability are enhanced, the interface verification accuracy and efficiency are improved, the interface case implementation efficiency is improved, the maintenance difficulty is reduced, and the user experience is improved. And the reusability of the interface case data is enhanced.

The application discloses a kind of data operation execution systems for enterprise level low code, specifically relates to low code development technical field, including atomic executor module, dependent parallel scheduling module and debugging sandbox module. Query, new, update, delete, rule check and custom function are uniformly encapsulated as standard atomic executor by atomic executor module, each executor configuration execution condition expression and trigger actual logic when condition is satisfied, while introducing adaptive timeout fuse mechanism to guarantee system stability. Dependent parallel scheduling module constructs data dependency graph between executors by regular matching, and based on topological sorting, non-dependent executors are divided into parallel groups. After priority sorting, they are executed in parallel through thread pool. The application solves the problems of low efficiency of low code platform data operation serial execution, dirty data in debugging process and opaque process, and realizes efficient parallel scheduling and safe and observable debugging experience.

The invention discloses an unauthorized vulnerability black box detection method based on a cross-request data dependency tracking mechanism. The method is used for identifying identity verification and authorization security problems with vulnerabilities in network applications. According to the method, a cross-request data dependency tracking mechanism is introduced to deduce an operation dependency relationship between pages, analyze feedback content and judge whether unauthorized reading or modifying operation exists or not, so that the detection of unauthorized vulnerabilities is realized. The method comprises the following specific steps: preprocessing: preparing an initial address of a target website and registering accounts of a common user and an attacker; constructing a cross-request data dependency graph, and establishing a dependency relationship among different operation requests; and vulnerability detection: tracking an attacker request through the cross-request data dependency graph so as to judge whether a vulnerability exists or not. According to the method for detecting the unauthorized vulnerability through the black box, the recall rate, the accuracy rate and the automation degree of unauthorized vulnerability detection are improved, and an effective unauthorized vulnerability detection tool is provided for software security analysts.

The invention relates to an electronic control firmware performance optimization method and related equipment, and the method comprises the following steps: carrying out the static semantic analysis of an original firmware code, and obtaining a firmware code symbol table; performing data stream tracking on the original firmware code based on the firmware code symbol table to obtain a data dependency graph; performing control flow sensitive analysis on the original firmware code based on the data dependency graph to obtain a control flow sensitive instruction sequence; performing loop expansion optimization on the control flow sensitive instruction sequence through an instruction scheduling engine technology to obtain an expanded instruction sequence; and performing register allocation on the original firmware code based on the expanded instruction sequence so as to reduce memory access times, improve the execution efficiency of the original firmware code, and solve the problems that traditional firmware development focuses on function implementation, neglects efficient matching between a code execution path and hardware resources, and improves the execution efficiency of the original firmware code. And the technical problem that instruction execution is delayed in a complex application scene is solved.

The application provides a reconfigurable computing architecture mapping method based on graph convolution and reinforcement learning, comprising the following steps: acquiring a data dependency graph and a coarse-grained reconfigurable array, and calculating a minimum start interval of the data dependency graph; preprocessing the data dependency graph according to the minimum start interval; simplifying a hardware model resource graph according to the structure of the coarse-grained reconfigurable array; taking a node set of the hardware model resource graph as an action space; adopting a reinforcement learning model based on a graph convolution network to map the preprocessed data dependency graph, so as to obtain a mapping result of a data dependency graph node on the hardware model resource graph; calculating a reward value of the mapping result; and when it is judged according to the reward value that the mapping result meets a successful mapping condition, outputting the mapping result. The application optimizes the layout and wiring of a loop on a reconfigurable computing array by using graph convolution and reinforcement learning, improves the parallelism of loop execution, and thus increases the mapping quality and reduces the execution time of image denoising.

A technique for arranging matrix multiplications for concurrent execution in an integrated circuit device may include obtaining a representation of a data dependency graph of a neural network model. The data dependency graph may include having an accumulation group (AG) pack of accumulation groups (AGs), in which each of the AGs has one or more matrix multipartition instructions. A representation of a memory location base partition constraint graph of the AG pack can be generated, and an AG row group constraint graph can be generated based on the memory location base partition constraint graph. The AGs of the AG pack can then be assigned to tiles in an integrated circuit device based on the AG row group constraint graph.

The application provides a vulnerability detection method based on graph embedding and a bidirectional gated graph neural network. First, function-level source code containing vulnerabilities and source code not containing vulnerabilities extracted from source code are obtained, all the source code is converted into a program dependence graph including a control dependence graph and a data dependence graph by using a source code analysis tool, and then the improved node2vec method is used to convert the program dependence graph into a code graph embedding representation, which contains both the graph structure information of the source code and the text structure information of the source code, and to a certain extent, the ability of the feature to represent nonlinear information is improved, and finally, the code embedding after the pretreatment is trained by using a bidirectional gated graph neural network model. The training result is applied to a target program to detect and evaluate the code vulnerability of the target program.

The application relates to the field of smart contract security and discloses a smart contract vulnerability detection method based on an operation code program dependency graph, which aims to obtain effective smart contract vulnerability related semantic information from the bytecode level and improve the accuracy of automatic detection of smart contract vulnerabilities. The method comprises the following steps: collecting a smart contract bytecode dataset and marking operation codes that may trigger vulnerabilities; simulating the execution of a smart contract operation code sequence, constructing a smart contract operation code control flow graph and a data dependency graph; constructing a smart contract operation code program dependency graph; taking the operation codes that may trigger vulnerabilities as key nodes, extracting vulnerability related program dependency graph slices, combining the marked information to construct a slice dataset; encoding the program dependency graph slices; training a smart contract vulnerability detection model based on a graph neural network to extract slice features and realize smart contract vulnerability detection.

The invention discloses a code searching method and system based on multi-structure chart fusion. An early fusion strategy is adopted, and three kinds of modal information of the abstract syntax tree, the data dependency graph and the control flow graph are fused into a function program graph to serve as function input of codes. Due to heterogeneity of edges of multiple graphs, a meta-path aggregation graph neural network is adopted to distinguish control flow, data dependence and syntax tree relations through different edge type processing mechanisms, code features of the graphs are extracted, and understanding of code semantic structures is enhanced. And meta-path selection is dynamically adjusted through a graph attention mechanism, weights are further allocated to different nodes, key information is highlighted, meanwhile, time sequence information in code snippets is captured, the code representation accuracy is improved, and the search capability is enhanced.

The invention discloses a PLC program vulnerability detection method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining a control program file through the output of a development platform, and obtaining a dangerous function fingerprint database based on the construction of the development platform; decompiling the control program file to obtain a dynamic function library; performing static stain analysis processing on the dynamic function library according to the danger function fingerprint library to obtain a stain variable set; performing symbolic execution processing on the control program file according to the taint variable set, and constructing a data dependency graph according to a symbolic execution result; and performing vulnerability detection processing on the data dependency graph according to a preset detection rule to obtain a detection result. The embodiment of the invention can improve the accuracy of vulnerability detection, and can be widely applied to the technical field of software security.

The specification discloses a code detection method and device, a storage medium and an electronic device, wherein the method comprises: acquiring a code to be detected, generating a syntax tree, determining a data flow and a control flow of the code to be detected based on the syntax tree, generating a data dependency graph based on the data flow, the data dependency graph comprising a plurality of nodes and data dependency edges corresponding to each node, generating a control flow graph based on the control flow, embedding the data dependency edges into the control flow graph, generating a hybrid dependency graph, performing code risk detection based on the hybrid dependency graph, and generating a code risk detection report.

This application discloses an automatic design method, device, medium, and product for complex special-purpose instructions (SPDIS) in hardware, relating to SPDIS hardware design, SPDIS instruction design, or instruction set extension. The method includes: analyzing the program to collect dynamic information; converting basic blocks in the program into a data dependency graph based on the compiler's intermediate representation and extracting seeds; using a genetic algorithm to obtain candidate programs that meet input / output constraints based on the seeds; using a specially designed cost model to score the candidate programs as a selection criterion; utilizing maximum weighted independent set analysis to overcome the overlap problem of candidate programs; and fusing the subgraphs corresponding to the SPDIS instructions to generate dedicated functional units, thus completing the automatic design of complex SPDIS instructions for hardware. This application can explore the similarities between basic blocks, discover common and frequently executed code segments in the program as SPDIS instructions; and the search can maximize the coverage of the target program, significantly accelerating the program.