14 results about "Data dependency graph" patented technology

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 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 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 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.

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 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 invention discloses an accelerated EPS calculation method, apparatus, server, and storage medium, comprising: calculating the ratio of edges to nodes in a directed acyclic graph of a circuit verification task; comparing the ratio with a ratio threshold, and when the ratio is less than the ratio threshold, performing data-level parallel processing using a SIMD instruction set; when the ratio is greater than the ratio threshold, decomposing the directed acyclic graph of the circuit verification task to obtain decomposed subgraphs until a subgraph constraint condition is met; calculating the subgraph ratio of edges to nodes in each decomposed subgraph, generating a task-level data dependency graph for the decomposed subgraphs based on dependencies; and comparing the subgraph ratio with a ratio threshold, and when the ratio is less than the ratio threshold, performing data-level parallel processing using a SIMD instruction set; otherwise, generating a subtask queue matching the computational processing unit based on the decomposed subgraphs and the task-level data dependency graph.

The invention discloses a code detection method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining a to-be-detected code, generating a syntax tree, determining a data flow and a control flow of the to-be-detected code based on the syntax tree, and generating a data dependence graph based on the data flow, the data dependence graph comprises a plurality of nodes and data dependence edges corresponding to the nodes, generating a control flow graph based on the control flow, embedding the data dependency edge into the control flow graph, generating a mixed dependency graph, performing code risk detection based on the mixed dependency graph, and generating a code risk detection report.

The application discloses a heuristic method suitable for instruction rearrangement of a multi-transmitter processor, comprising the following steps: in a data preparation stage, obtaining runtime parameters of each instruction according to instruction set information and functional unit information, obtaining a data dependency graph according to data dependency relationships between instructions, processing the data dependency graph to obtain transmission priorities of the instructions, and then in a simulation running stage, ensuring that the instruction transmission process meets constraint conditions of a parallel transmission instruction number, an available functional unit number and a pipeline function limit. The application also provides a device suitable for instruction rearrangement of the multi-transmitter processor. The application solves the problem of instruction parallel optimization caused by the performance decline of an application program due to the migration of assembly codes between different generations of a domestic Shenwei many-core processor, improves the program transplantation efficiency, and solves the adaptability problem of an application assembly program library to a dual-transmission pipeline microstructure compared with the prior art.

The invention relates to a test case generation method and system, and the method comprises the steps: carrying out the change weighting of all change line numbers, change timestamps, change depths and change operators, which are extracted from a Git change record of a current version, through employing a time exponential decay model; meanwhile, a penalty factor of author diversity entropy generated by a change operator is introduced in the weight change process to obtain row-level change popularity, and a code calling graph, a code inheritance graph and a data dependency graph are extracted from a complete code library of the current version through a static analysis tool to construct a code heterogeneous graph; the method comprises the following steps: establishing a large language model, introducing line-level change popularity and carrying out weight aggregation to generate an influence map, extracting and executing a hotspot method from the influence map to generate a to-be-covered path, and inputting the to-be-covered path into the large language model to output a recall test case and a newly-added test case. Therefore, the method not only has the capability of generating the new test case, but also ensures the recall rate and the accuracy of the test case.

The invention discloses a communication method based on resource awareness and asynchronous parallel. The method comprises the following steps: calculating a key resource consumption dimension in a big language model training process; capturing dependency relationships of all layers in a model operation process to form a block-level data dependency graph; loads are distributed to all the nodes in a balanced mode; and a non-interlaced 1F1B scheduling strategy is adopted to realize parallel execution of data transmission and model calculation. According to the embodiment of the invention, by monitoring the computing power and the memory capacity of the CPU nodes and the computing complexity and the memory demand of each layer of the neural network in real time, the model is reasonably allocated to different nodes according to the calculated amount and the memory consumption by adopting the adaptive segmentation algorithm. The limitation of traditional static segmentation is broken through, and the problem of load imbalance in a heterogeneous environment is effectively solved; through an asynchronous queue and non-blocking communication technology and fusion of a double-buffer and non-staggered 1F1B scheduling strategy, data transmission and model calculation can be carried out in parallel, the defect of idle resources is effectively overcome, and communication overhead is greatly reduced.

This application belongs to the field of wind turbine generator control technology, and particularly relates to a method, system, device, and medium for optimizing inter-core task scheduling based on a multi-core controller. The method includes: dividing the wind turbine generator into several core control tasks according to its operational requirements, determining corresponding task scheduling units, and constructing a data dependency graph; monitoring the real-time operating status parameters of the wind turbine generator and the current load status of each processing core of the multi-core controller, and determining whether the performance of the processing core is abnormal based on the current load status and fault indication signals to trigger fault recovery scheduling, or acquiring dynamic sensing information; inputting the data dependency graph and dynamic sensing information into a scheduling decision model of a state-aware graph neural network to calculate and generate an initial task allocation scheme; executing the initial task allocation scheme to obtain performance feedback, and adjusting the scheduling weight parameters based on the performance feedback to update the initial task allocation scheme. This application improves the efficiency of wind turbine generator task scheduling.