1430 results about "Vulnerability detection" patented technology

The invention relates to the technical field of software engineering, in particular to a source code detection method, which comprises the steps of source code preprocessing, code dependency analysis, semantic understanding and analysis, code quality evaluation, dynamic behavior simulation, intelligent vulnerability detection and the like to realize comprehensive detection of potential vulnerabilities and quality problems in source codes. A support vector machine and a K-means clustering algorithm are combined, known and unknown vulnerabilities can be effectively recognized, in addition, a continuous optimization feedback mechanism is introduced, a mixed vulnerability detection model can be continuously optimized according to repair operation and feedback information of a developer, the detection accuracy is improved, and the coverage range is expanded. The generated comprehensive detection report covers code structures, vulnerability positioning, quality problems and optimization suggestions, supports integration of various output formats and development tool chains, helps developers to efficiently repair problems, and improves code quality and project progress; according to the method, the comprehensiveness, the self-adaptability and the user experience of code detection are effectively improved.

The invention discloses an unmanned aerial vehicle flight control system vulnerability detection method based on data flow analysis and LLM, and belongs to the technical field of intelligent software testing. Comprising the following steps: extracting a code function module associated with user operation in an unmanned aerial vehicle flight control system through a data flow analysis method, and establishing an operation-code mapping relation library; generating a structured natural language semantic description for each function module code by adopting a large language model LLM, and forming a multi-dimensional semantic feature vector; based on correlation analysis of multi-module semantic features, a combined test scene is constructed, and a natural language test case is generated; the natural language test case is converted into an executable test code through reverse semantic mapping, and coding reconstruction of test logic is completed; and executing a test code and capturing a runtime log in an unmanned aerial vehicle simulation environment, and performing vulnerability feature extraction and root cause positioning by using a large language model. According to the method, the efficiency is improved, and meanwhile, the deep coverage test of a complex interaction scene is supported.

The invention discloses a big language model reasoning and retrieval enhancement-based vulnerability description and repair suggestion generation method, which comprises the following steps of: constructing a vulnerability knowledge base by integrating vulnerability databases such as CWE and CVE and an external knowledge source, providing prompt information of professional knowledge for a big language model, preprocessing a to-be-tested code by utilizing a code analysis tool, and generating a big language model reasoning and retrieval enhancement-based vulnerability description and repair suggestion. And extracting vulnerability knowledge most related to a to-be-detected code from the vulnerability knowledge base through semantic matching and code matching, generating detailed vulnerability description and repair suggestions by utilizing a large language model based on the related vulnerability knowledge obtained in the retrieval enhancement stage, and optimizing a generation result through a thinking chain technology. By combining the semantic comprehension ability, the retrieval enhancement technology and the reasoning enhancement technology of the large language model, detailed and targeted vulnerability description and repair suggestions can be quickly generated, the vulnerability repair efficiency is remarkably improved, and the method can be flexibly applied to existing vulnerability detection tools and is suitable for various programming languages and vulnerability types.

The invention relates to the technical field of software reliability guarantee, in particular to a code vulnerability detection method based on efficient parameter fine tuning of a code pre-training model, which comprises the following steps: preprocessing source codes of a vulnerability data set; s2, converting the preprocessed source code, and extracting a token sequence and a code attribute graph path corresponding to the source code; s3, constructing a code vulnerability detection model, wherein the code vulnerability detection model comprises a semantic feature extractor, a structural feature extractor, a feature fusion layer and a classifier; s4, jointly training a code vulnerability detection model by using the token sequence corresponding to the extracted source code and the code attribute graph path; and S5, through the optimized code vulnerability detection model, outputting a result that whether the source code to be detected has vulnerabilities or not. According to the method, the model is finely adjusted through efficient parameter fine adjustment, training resources are saved, good performance is obtained, and the code vulnerability detection efficiency and accuracy are improved by fusing and learning semantic and structural features of source codes at the same time.

The present disclosure relates to a system and method for vulnerability localization based on deep learning, which comprises, at a minimum, a processor configured to: analyze a code file under detection to obtain a first abstract syntax tree devoid of semantic information; build upon the first abstract syntax tree by incorporating data-flow edges and / or control-flow edges, thereby forming a second abstract syntax tree with semantic-flow enhancement; split the second abstract syntax tree to obtain a plurality of second abstract syntax sub-trees; and input these second abstract syntax sub-trees into a pre-established vulnerability detection and localization model. Compared with existing code vulnerability detection methods, the present disclosure employs a semantically-enhanced abstract syntax tree and finely-grained segmentation thereof, enabling both the efficient detection and accurate localization of code vulnerabilities, characterized by swift detection rates, low false positive rates, and commendable interpretability of the detection results.

A method of code vulnerability detection includes obtaining a set of patches from at least one code repository for a vulnerability record of a vulnerability of a software component, applying the set of patches to obtain a modified set of code files modified by the set of patches, and inferring a vulnerable code section set according to the modified set of code files. The vulnerable code section set is a subset of the modified set of code files. The method further includes obtaining a vulnerable version list of a plurality of versions of the software component comprising the vulnerability, validating a version in the vulnerable version list as having the vulnerable code section set, and processing a target software comprising the version using the vulnerable code section set.

Systems and methods for detecting vulnerabilities in software in real time. In some aspects, the system receives a training dataset, including first source code, first runtime data flow, a set of sample vulnerable libraries, and a set of sample replacement software libraries. Using the training dataset, the system trains a vulnerability detection model. The system receives second source code being tested in a development environment and collects the second source code and second runtime data flow. The system processes the second source code and second runtime data flow using the vulnerability detection model to identify novel vulnerable software libraries and / or known vulnerable software libraries. The system generates suggestions to replace the known and / or novel vulnerable software libraries. In response to detecting a user choosing a first replacement library in place of a first vulnerable library, the system replaces the first vulnerable library with the first replacement library.

The invention discloses a deep learning artificial intelligence security vulnerability detection platform, and relates to the technical field of intelligent detection, and the platform comprises an information processing module which collects heterogeneous data in real time, carries out the labeling, format unification and modal aggregation processing of the data, and generates a sample set; the feature learning module is used for performing feature unwrapping on the sample set by using a variational auto-encoder, extracting modal data features and potential space representation learning, and outputting a potential space vector; the response generation module is used for generating a vulnerability response strategy through a modal consistency verification and response template matching mechanism based on the vulnerability risk level vector in combination with a response generation engine; and the repair feedback module is used for executing automatic vulnerability repair operation in combination with federal reinforcement learning and Bayesian optimization, performing feedback optimization according to an execution result, and outputting the vulnerability repair operation and a feedback result. According to the method, the response strategy is combined with intelligent matching of the real-time risk level, so that the accuracy and adaptability of vulnerability repair are improved.

The invention discloses an information security risk assessment method based on an API. The method comprises the steps that API assets in an enterprise are obtained through flow analysis; identifying and positioning the log file or sensitive data in the file by using a sensitive data identification model; integrating the vulnerability rule set, and detecting vulnerability vulnerabilities existing on the API through an API vulnerability detection model; a behavior risk automatic monitoring engine is set, and real-time monitoring and early warning are carried out on the behavior of abnormally calling the AP I to obtain data; constructing a network attack monitoring model; drawing an API risk link relationship based on a risk monitoring model trained by historical monitoring data, and speculating a suspicious data leakage path in combination with clue association ranking analysis; performing comprehensive trace reserving on the API data use behavior; and calculating the risk by using a matrix method, and generating an API information security risk assessment report. Through the method, a comprehensive API security risk assessment system can be established, API security risks can be effectively identified and managed, and enterprise data security is protected.

The present invention relates to the technical field of blockchains. Disclosed are a smart-contract vulnerability detection method and system based on a hypergraph and multi-teacher distillation. Specifically, the smart-contract vulnerability detection method based on a hypergraph and multi-teacher distillation comprises: using a vulnerability detection open-source tool to perform vulnerability detection and analysis on source codes of a smart contract, and marking labels for the source codes of the smart contract; then, using a hypergraph to dynamically capture a deeper and richer relationship between the source codes, key functions and key variables in the smart contract, and on the basis of the hypergraph, training a plurality of vulnerability detection teacher models having excellent performance; and finally, using the plurality of vulnerability detection teacher models to perform knowledge distillation, so as to obtain a vulnerability detection student model, such that the vulnerability detection student model can also obtain knowledge without exposure to large-scale data, and thus directly applies the knowledge to vulnerability detection of new data. The smart-contract vulnerability detection method based on a hypergraph and multi-teacher distillation in the present invention can effectively detect various types of smart-contract vulnerabilities, and can effectively reduce the occupancy of storage space and improve the running speed.

The invention relates to the technical field of vulnerability detection, and provides a fine-grained vulnerability detection method and system based on semantic perception sparse attention, and the method comprises the steps: analyzing a to-be-detected source code, and generating an abstract syntax tree, a control flow graph and a program dependency graph; carrying out weighted fusion on graph representations of the abstract syntax tree, the control flow graph and the program dependency graph, and integrating into a code attribute graph; for the code attribute graph, processing the code attribute graph by adopting a semantic perception sparse attention method combined with sparse attention, a learnable Token pruning method and Top-k interaction frequency to obtain a Token attention score; and based on the obtained Token attention scores, accumulating the attention score of each Token in each row of the code, and detecting and determining a statement where the vulnerability code is located. According to the detection method disclosed by the invention, the capability of modeling code semantic information and capturing a structured dependency relationship can be effectively improved, and meanwhile, the calculation complexity is remarkably reduced.

The invention discloses an intelligent contract vulnerability detection and repair system based on a heterogeneous graph neural network, and belongs to the technical field of block chain security, and the system comprises a contract analysis module, a multilayer graph construction module, a heterogeneous graph neural network module, a vulnerability feature library, a vulnerability recognition engine, an automatic repair module and a visual interface. After the source code of the intelligent contract is input, code analysis and standardization are completed by a contract analysis module; the multi-layer graph construction module constructs a contract internal heterogeneous graph, an inter-contract interaction graph and an ecosystem relation graph based on a graph theory; the heterogeneous graph neural network module learns a vulnerability feature mode; the vulnerability recognition engine combines the vulnerability feature library to realize vulnerability classification and risk assessment; the automatic repairing module generates a repairing scheme; and the visual interface realizes detection progress monitoring, result display and encrypted report export. The intelligent contract vulnerability detection and restoration system based on the heterogeneous graph neural network provided by the invention provides technical support for block chain digital asset security and ecological stability.

The invention discloses a PHP taint type vulnerability detection method based on a heterogeneous graph neural network, and belongs to the field of software security. The method comprises the following steps: performing annotation removal, variable naming standardization and character string standardization processing on a PHP source code through a code preprocessing module to generate a standardized code; based on a vulnerability sub-attribute graph extraction module, reversely tracking vulnerability sinks to a taint source, extracting a simplified vulnerability sub-attribute graph, and removing redundant nodes and edges; fusing BERT semantic features and node type features through a graph node embedding module to generate an initial embedding vector, and constructing a heterogeneous graph comprising an abstract syntax tree edge, a program flow graph edge and a control dependence graph edge; a heterogeneous graph neural network vulnerability detection module is adopted to perform independent feature aggregation on multiple types of edges, dynamic weighted fusion is performed in combination with an attention mechanism, and key nodes are screened through Top-k graph pooling; and finally, inputting the graph-level features into a classifier to realize vulnerability detection.

An end-to-end approach for (i) identifying potential vulnerabilities in software applications, including security vulnerabilities, (ii) verifying / confirming the potential vulnerabilities as actual vulnerabilities and (iii) in response, identifying the necessary remedial actions necessary to eliminate or at least mitigate the vulnerabilities. An intelligent agent is implemented that is configured to detect a change to the application's code or computing environment and, as a result of detection of changes to the code or computing environment, identify potential vulnerabilities, verify / confirm the potential vulnerabilities as actual vulnerabilities through determination / creation of vulnerability test cases and automatically identify the necessary remedial actions necessary to eliminate or mitigate the vulnerabilities.

The invention is applicable to the technical field of vulnerability detection, and provides a power system software security vulnerability dynamic and static combined detection method, which comprises the following steps: acquiring static source code data of power system software and dynamic behavior log data during operation; generating a hierarchical abstract syntax tree and structured time series data; performing multi-dimensional feature extraction on the static source code data and the dynamic behavior log data based on a vulnerability knowledge graph of the power system; generating a domain constraint confrontation sample based on the power protocol features and the abnormal features during operation, and inputting the domain constraint confrontation sample into the constructed hybrid detection model for confrontation training to obtain a trained hybrid detection model; and performing automatic detection on the power system software by utilizing the trained hybrid detection model, and outputting a detection result containing a static code defect position and a dynamic attack path. The full-life-cycle accurate detection of the software vulnerability of the power system is realized, and the network security protection capability of the power system is improved.

A live mapping framework may be generated for large enterprise- wide networks and systems. The live mapping framework includes identification of key attributes of every device connected to the network. The key attributes may include device type, installed software packages, network connection information, device usage information, and other information. Frequent updates to the live mapping framework and trending over time enable the identification of system vulnerabilities. Generative and explainable Al may additional be used for penetration testing of virtual replicas of the enterprise- wide networks, leading to the generation of solution sets addressing the identified vulnerabilities.

Improved vulnerability detection in a cloud computing environment may be achieved by monitoring, by an agent executing in a compute asset of the cloud computing environment, one or more events associated with code deployed in the cloud computing environment; based at least in part on the one or more events, determining whether the code is active in the cloud computing environment; in response to the code being active, scanning the code to identify one or more vulnerabilities; and performing a notification in response to identifying one or more vulnerabilities.

The invention provides a firmware logic vulnerability detection method and system based on a big language model thinking chain, electronic equipment and a computer readable storage medium. The method comprises the following steps: constructing a business logic vulnerability knowledge base by adopting a first big language model; obtaining basic data of a firmware business program to be detected to construct a business object knowledge graph; based on the business logic vulnerability knowledge base, adopting a retrieval enhancement generation algorithm to obtain a sensitive control flow and a business logic vulnerability normal form corresponding to the to-be-detected firmware business program; based on the business logic vulnerability normal form and the sensitive control flow corresponding to the to-be-detected firmware business program, adopting a large language model thinking chain technology to iteratively generate multi-level cue words; and based on the multi-level cue word and the business object knowledge graph, adopting a second large language model to obtain a logic vulnerability detection result. According to the method and the device, various service logic vulnerabilities in the Internet of Things terminal firmware can be detected.

The invention provides an Internet of Vehicles vulnerability management method, system, device and medium based on a block chain, and relates to the technical field of vehicle networks, the method can comprehensively identify potential safety risks of vehicles through a dynamic and static combined vulnerability detection mechanism, and generates a structured vulnerability report; hash abstract chaining evidence storage is performed on key data of reports and subsequent repair links by using a block chain, so that the whole process of vulnerability discovery, analysis and repair is ensured not to be tampered and traceable, and the authenticity and credibility of data are improved; the integrity of the report is verified at the cloud end, and an AI analysis engine is combined to associate a CVE database and threat intelligence, so that intelligent generation and decision support of a repair scheme are realized; the hash abstract on the chain of the OTA patch is verified at the vehicle end, so that the credibility of the patch source and the integrity of the content are guaranteed; and finally, through feedback of an installation result and secondary uplink archiving, complete closed-loop management from vulnerability discovery to repair verification is formed.

The invention discloses a vulnerability hidden danger intelligent detection method based on a large model, and the method comprises the steps: firstly carrying out the global static analysis of a source code set, constructing a complete call graph and a complete data flow graph of a program, and forming a structured code knowledge graph; and then, aiming at the identified candidate vulnerability slices, based on the maps, carrying out accurate context retrieval and enhancement, converting key information such as a call chain and a data traceability path which are strongly related to the vulnerability slices into natural language description which can be understood by a large language model, and injecting the natural language description into cue words, so that missing global context information is provided for the model. And the defect of complex code analysis capability is overcome. In this way, the problem that an attention mechanism loses efficacy in remote code association is solved, and the accuracy and reliability of vulnerability detection are remarkably improved.

The invention discloses a cross-chain smart contract vulnerability detection method and system based on multi-feature fusion learning. The method comprises the following steps: collecting a cross-chain smart contract vulnerability data set for cleaning and labeling; feature extraction is carried out from the source code and the byte code, an abstract syntax tree (AST) is extracted from the cleaned source code, a basic control flow graph (CFG) is extracted from the byte code, and a cross-chain control flow graph (xCFG) is constructed; carrying out feature representation on AST and xCFG, generating a graph vector through a graph neural network (GNN), generating a semantic vector through CodeBert, and fusing the semantic vector into a feature fusion vector; performing model training and detection, taking the generated vectors as training data and test data, obtaining a cross-chain smart contract vulnerability detection model by adopting Transform-FC model training data, and finally evaluating model performance through accuracy, recall rate, precision rate and F1 value. According to the method, the structural features and semantic features of the codes can be effectively fused, potential vulnerability information in the codes can be fully mined, the recognition capability of the model for cross-chain vulnerabilities can be enhanced, and the accuracy and reliability of the cross-chain vulnerability detection model can be improved, so that the security of a block chain system can be more efficiently guaranteed.

The invention provides a network security vulnerability detection method and system based on artificial intelligence, relates to the field of network security, and is used for solving the problems of poor network security vulnerability detection accuracy and low response effectiveness in the prior art. The method comprises the following steps: constructing a network multilayer graphic model; identifying different types of suspected network security vulnerabilities in the network multilayer graphic model by adopting different identification methods; in combination with the context information of the suspected network security vulnerability, performing dynamic evaluation on multiple indexes of the suspected network security vulnerability, and determining whether the suspected network security vulnerability is a real network security vulnerability according to an evaluation result; and generating an initial security response strategy corresponding to the real network security vulnerability in combination with a multi-level response planning system, and optimizing the initial security response strategy in combination with a historical response effect and user feedback information to obtain a target security response strategy. According to the technical scheme provided by the invention, the accuracy and response effectiveness of network security vulnerability detection are improved.

The invention relates to a network security vulnerability intelligent detection method based on artificial intelligence and big data, and the method comprises the steps: carrying out the dynamic time synchronization processing of multi-modal network security data, and generating a multi-source data flow with aligned time sequences through cross-modal correlation analysis; extracting cross-modal features from the data stream, and performing semantic fusion on the cross-modal features in combination with a vulnerability knowledge graph to generate a multi-dimensional feature vector; training the multi-dimensional feature vector through a hybrid model to obtain a vulnerability detection model, detecting real-time network behavior data by using the model, and outputting a vulnerability probability and an abnormal risk score; and performing automatic vulnerability verification according to the vulnerability probability and the abnormal risk score to obtain a verification result, and updating the vulnerability detection model according to the result. The system can effectively improve the accuracy, timeliness and stability of network security vulnerability detection and reduce the false report and missing report rate through multi-modal data collaboration, hybrid model dual detection and closed-loop optimization mechanisms.

The invention relates to the field of intelligent contract vulnerability detection, in particular to an intelligent contract vulnerability detection method based on code graph fusion analysis and a graph matching network. The method comprises the steps of constructing an intelligent contract source code data set; through static analysis, extracting three different code feature information including a control flow diagram, a data flow diagram and an abstract syntax tree from the source code of the smart contract; carrying out information integration on the three extracted code features, and constructing a code graph covering wider features; on the basis of the generated code graph, a GraphCodeBerT model is used for pre-training; vectorizing a code graph which is output by the pre-training model and contains structural and semantic features through a graph embedding network; and learning the feature representation of the contract pair by using a cross-graph attention mechanism of a graph matching network to realize specific vulnerability detection of the smart contract source code. According to the invention, potential vulnerabilities can be accurately identified.

The invention discloses an intelligent contract vulnerability detection method based on a heterogeneous graph attention network. According to the method, firstly, the source code of the intelligent contract is preprocessed, the SCIR of the code of the intelligent contract is constructed, the complexity of the code of the contract is reduced, and vulnerability features are enriched; and constructing an intelligent contract code attribute graph SCPG based on SCIR, integrating various code graph structures such as an abstract syntax tree and a control flow graph, and comprehensively describing syntax and semantic features of the contract. And then constructing an intelligent contract code heterogeneous graph SCHG on the basis of the SCPG, optimizing code graph structure representation, and realizing high-quality modeling of node features. And finally, detecting the vulnerability of the smart contract by using a customized multi-layer heterogeneous graph attention network model MHGAN. The intelligent contract vulnerability detection method based on deep learning makes up for the defects of an existing intelligent contract vulnerability detection method based on deep learning, effectively improves the accuracy of intelligent contract vulnerability detection, and is excellent in the interpretability of the detection result.

The invention provides a source code vulnerability detection method and device based on a fusion code attribute graph and electronic equipment, and the method comprises the steps: obtaining to-be-detected source code data, extracting the code representation of the source code data, and converting the code representation into the fusion code attribute graph; inputting the graph coding features of the fused code attribute graph into a vulnerability detection model to obtain a vulnerability detection result; and interpreting the vulnerability detection result based on the interpretable model to obtain a fine-grained interpretation result of the code statement related to the vulnerability in the vulnerability detection result. Compared with a traditional code attribute graph, the fusion code attribute graph has more advantages in the aspect of covering a grammar structure and a function calling relation, the context correlation of codes can be reflected more comprehensively, and support is provided for accurately recognizing complex code vulnerabilities; the interpretable model interprets the vulnerability detection result, points out code statements related to the vulnerability, provides a fine-grained interpretation result, and can help subsequent developers to quickly repair the vulnerability.

The invention discloses a code fingerprint-based open source component identification and vulnerability detection method, which comprises the following steps of: receiving a local or remote code, extracting a difference file, generating an AST and constructing a code attribute graph; sHA-256 Hash fingerprints and GNN semantic fingerprints are calculated for the function level sub-graphs to form mixed fingerprints, accurate matching is conducted through a Bloom filter, semantic matching is completed through nearest neighbor, and a component version is determined through a distribution difference algorithm. The method comprises the steps that firstly, a component identifier is mapped into a PURL or an SWID, an OSV / NVD library is inquired to obtain a CVE, comprehensive risks are calculated in combination with CVSS, EPSS and dependency depth, and an SBOM and a vulnerability report conforming to CycloneDX or SPDX are output. The method is high in speed and high in accuracy, and the open source risk can be automatically treated in continuous integration.

An industrial security system leverages generative artificial intelligence (AI) to automate the process of identifying software or hardware insecurities on industrial assets, generate recommendations for mitigating these vulnerabilities, and, where appropriate, deploy countermeasures to the vulnerable assets. By leveraging automated asset discovery, real-time asset and network monitoring, and generative AI-assisted vulnerability detection and remediation, the system can reduce the amount of time spent by security administrators in identifying and closing security vulnerabilities within their plant environments, and can alert administrators of potential security issues before those issues become critical.

The invention relates to the technical field of information processing, in particular to a supply chain cross-packet vulnerability detection method, device and equipment and a storage medium. A vulnerability packet name, a sensitive API, a trigger parameter and vulnerability description are integrated into tetrad information; if so, performing cross-packet call chain analysis on the source code file by using a cross-packet chain reachability analysis algorithm, obtaining a function call sequence of the sensitive API based on a cross-packet call chain analysis result, realizing vulnerability detection on a cross-packet call chain, generating a vulnerability verification code based on tetrad information by using a preset large language model, and performing vulnerability verification on the vulnerability verification code. The method comprises the following steps: establishing a function call sequence of a bug verification code, verifying the accessibility of the bug verification code in the function call sequence, determining the bug confidence according to the energy consumption condition of a large language model, and generating bug alarm information when the accessibility verification result is that the bug is accessible and the bug confidence is high, thereby realizing double judgment of the bug, reducing the false alarm rate of the bug and improving the user satisfaction.

A system for dynamic vulnerability detection and remediation is provided. The system includes a memory device and at least one processor coupled to the memory device. The at least one processor is programmed to: (a) store within a database an inventory of computer assets included within a computer ecosystem; (b) retrieve a vulnerability report including vulnerability definitions; (c) analyze the database to identify a potential vulnerability by comparing the computer assets stored within the database to the vulnerability definitions; (d) upon detecting the potential vulnerability, determine a service owner associated with the computer assets identified as being involved in the potential vulnerability; and (e) provide content to a user computing device associated with the service owner causing the user device to display a notification alert advising the service owner of the potential vulnerability and providing a remediation plan to address the potential vulnerability.