153 results about "Reverse engineering" patented technology

The present disclosure provides systems and methods that support identification of vulnerabilities within a dataset, such as an unknown binary, using customizable pipelines and workflows. The pipelines may be configured to apply various analysis techniques, including reverse engineering, machine learning / artificial intelligence, and other techniques to elicit various types of data and information from the unknown binary. The pipeline may include stages configured to analyze outputs of prior stages of the pipeline detect whether vulnerabilities or malicious code are present. Different stages of the pipeline may also be configured to initiate additional workflows for further analysis of the binary or intermediate representations of the binary output by prior stages of the pipeline. Additionally, the disclosed systems and methods provide functionality that enables generation of a reverse software bill of materials (SBOM) from an unknown binary or confirmation of contents of an existing SBOM.

The invention relates to the technical field of material testing, and discloses a coating performance simulation method based on high and low temperature circulation and prestress loading. The method comprises the following steps: constructing a coating matrix composite geometric model containing a microporous structure by acquiring actually measured working condition data of a pipeline or a part, and quantifying surface roughness and interface curvature characteristics in combination with reverse engineering; a dynamic temperature-pressure coupling equation is generated based on the thermal expansion effect of a high-pressure medium, a micropore optical load is converted into equivalent heat flux density through a ray tracing algorithm, transient boundary conditions of a temperature field and a stress field are associated, a crack initiation path is simulated through an extended finite element method, and a life attenuation curve is predicted in combination with a Paris formula. And material parameters are dynamically corrected based on experimental data, and a multi-parameter collaborative optimization model is generated. According to the method, the problems of interface stress evaluation distortion and life prediction deviation under the combined load are solved, and multi-physics field co-simulation support is provided for coating compression resistance optimization.

The invention relates to the technical field of information security, in particular to a user information protection method and device based on a dynamic desensitization strategy, and the method comprises the steps: determining sensitive data and a sensitivity score thereof through a named entity recognition model and a sensitivity scoring algorithm, extracting scene parameters, and determining a scene risk score through a risk quantification algorithm; and then a dynamic desensitization strategy generation algorithm is called to generate a target desensitization strategy, and finally, the strategy is used for desensitization and a reversible desensitization data identifier is generated. Through the series of steps, the desensitization strategy is dynamically generated according to different use scenes, the problem of scene adaptability deficiency caused by static desensitization strategy stiffness is solved, meanwhile, the reversible desensitization reduces the security risk caused by reverse cracking of rules, excessive desensitization is avoided, and the data availability is improved.

The invention relates to the field of computer graphics, reverse engineering and additive manufacturing, discloses a multi-modal fusion and physical constraint three-dimensional point cloud geometric modeling system and method, and aims at overcoming the defects that traditional three-dimensional reconstruction entity attributes are missing, key features are prone to being lost, semantic understanding does not exist, and process automation is low. The system comprises a point cloud preprocessing module, a semantic segmentation and recognition module, a mixed geometric reconstruction module, a multi-component assembly and physical constraint solution module and an entity model output module. The method comprises the following steps: denoising and sampling an original point cloud, realizing semantic and instance double segmentation through a Point Net + + network, endowing a geometry-function composite label, completing hybrid reconstruction through improved Poisson reconstruction, parameterized B-Rep fitting and standard component library matching, and outputting a multi-format engineering available model after physical constraint verification and automatic correction of a violation model. According to the method, automatic, high-fidelity and high-practicability conversion from the point cloud to the engineering-level entity model is realized, and the method is adaptive to multiple industrial application scenes.

The invention relates to an anti-reverse confusion method and system for an end-side equipment deep learning model, and the core process comprises the steps: firstly inputting an original model obtained through the training of a model training frame into a deep learning compiling frame, and extracting three types of key information, namely, a model operator, a topological structure, parameters and dimensions, through the characteristics of a compiler; then constructing a feature analysis module to evaluate model features, dynamically matching a confusion scheme from a strategy library, and balancing safety and performance; the confusion module is embedded into a plurality of different levels such as a computational graph level, an operator template level and tensor intermediate expression through a hierarchical compiling mechanism, and a complex scheme can be jointly implemented across multiple levels; and finally, the compiler synchronously completes confusion reinforcement when generating the target code. According to the method, hardware adaptation is not needed, low-overhead confusion is achieved through a native pass mechanism of a compiler, fine-grained customized protection is supported, a model structure, parameters and computational logic can be effectively hidden, reverse engineering attacks can be resisted, and the method is particularly suitable for end-side equipment scenes with limited computing power.

The invention discloses a method and system for reversely generating a design book through codes driven by a large language model, and relates to the field of software engineering. According to the method, code engineering is analyzed through a grammar analysis technology and a big language model, the incidence relation of visible explicit and implicit codes is mined, and a knowledge graph is formed. And function points are mined from a project by applying heuristic search in combination with a vector clustering technology. And relying on the knowledge graph, a summary design book and a detailed design book are reversely generated for code engineering by utilizing a large language model, so that the technical difficulty of maintenance and migration of a software system is reduced in an auxiliary manner.

A system and method for an AI-based Automated Software Application Development Platform which intelligently gathers requirements from a business analyst and generates a runnable software application from those requirements. The AI-based platform enables the business analyst with no technical knowledge to visually model their requirements rapidly without requiring any programming Using the abstract requirements architecture and the platform, the analyst creates requirements models for the application's business processes and business tasks for each of the processes, including defining the supporting information needs. The system intelligently assists the user in defining the information needs for each of the business processes and their tasks. Once the requirements models are created by the analyst with the help of the platform, the platform automatically designs and creates an appropriate persistent canonical data model for the application from the defined information needs. The platform then creates a runnable software application. The system also provides a template for the user to input their requirements in descriptive text, and accepts requirements document for applications as written free form text or text documents written in English as input, and in a non-English language that the system automatically translates them into English. The system provides a method for reverse engineering an existing application, including generating a requirements document for the existing application such as a legacy modernization or digital transformation an old applications for redesigning, updating or revising them to accommodate a new requirements set for such applications.

The invention discloses a data privacy trusted computing method and system, and relates to the technical field of data security and privacy protection.The method comprises the steps that a data privacy trusted computing engine is established in a federal learning mode; performing backdoor defense on the data privacy trusted computing engine by adopting a multi-stage federated learning backdoor defense mode; the multi-stage federated learning backdoor defense mode comprises the steps of screening a model uploaded by a user by adopting a robust aggregation algorithm, detecting whether the model has a backdoor by adopting trigger reverse engineering and a backdoor judging device, and repairing the model with the backdoor by adopting a backdoor forgetting technology; and constructing a federated learning backdoor defense system according to processing results of a robust aggregation algorithm, trigger reverse engineering, a backdoor judger and a backdoor forgetting technology. According to the method, the technical problems of low heterogeneous data processing efficiency and insufficient backdoor defense are effectively solved by constructing a multi-modal federal learning architecture and a multi-level self-adaptive defense system, and reliable technical support is provided for data security sharing in the power industry.

The invention discloses a continuous anomaly detection method based on playback generation, which comprises the following steps of: learning class semantic embedding of given class historical data in a feature space of a pre-training diffusion model by adopting reverse engineering; for an original image of the historical data, generating a mask image corresponding to the original image of the historical data, designing an MLP layer to learn mapping from a mask to a spatial feature, and obtaining a spatial feature aligned with the semantic feature in size; for a given class, training to obtain an optimal semantic embedding and MLP layer and storing the optimal semantic embedding and MLP layer, and randomly storing M mask images at the same time to jointly form conditional features of the given class; and replaying historical category data by using the condition features to realize controllable generation in two aspects of semantics and space. According to the method, the diffusion-driven generation and playback method is applied to the field of continuous anomaly detection for the first time, controllable data playback in the two dimensions of semantics and space is achieved, and therefore real and diversified historical data are generated.

The invention provides a static arc contact workpiece anti-ablation uniformity sample abnormal image detection method and system, and the method comprises the steps: carrying out the modeling of the physical behaviors of a static arc contact workpiece under different arc conditions, and generating a multi-physical field coupling simulation model and a theoretical reference image; acquiring an actual surface image, extracting advanced features by adopting various quantitative indexes and a convolutional neural network based on the theoretical reference image, performing comparative analysis, and generating a matching degree evaluation result; performing anomaly detection on the evaluation result, identifying an abnormal region with non-uniform ablation resistance, and generating an anomaly identification result; and according to an abnormity identification result, utilizing a reverse engineering principle and combining a particle swarm optimization algorithm to reconstruct an arc behavior mode causing abnormity, inversely adjusting input parameters through a simulation model until abnormity is reproduced, obtaining preliminary cause diagnosis information, and generating a detailed cause diagnosis report. According to the invention, the capability of detecting and improving the ablation resistance uniformity of the static arc contact workpiece is improved.

The invention discloses a graph neural network-based cross-architecture binary code similarity detection method, which comprises the following steps of: respectively implementing original feature extraction operation on an open source software binary file and a firmware binary file by utilizing a reverse engineering tool to obtain basic feature data of the open source software binary file and the firmware binary file; the control flow diagram of the open source software and the firmware is converted into an embedded representation through an embedded network, the open source software generates an embedded representation table, and the firmware generates a corresponding embedded representation; and storing the open source software embedded representation into an open source software database, storing the firmware embedded representation into a firmware database, and finally, comparing the similarity of data in the database to complete the similarity detection of the cross-architecture binary codes so as to screen out code fragments possibly having potential safety hazards in the firmware. According to the method, the similarity of the cross-architecture binary codes can be effectively detected, and the third-party codes with security risks in the firmware of the Internet of Things equipment can be accurately detected.

Systems and methods for data obfuscation are provided. Data obfuscation is needed when protecting an algorithm from reverse engineering attempts. The data is obfuscated by requesting more data from the data steward than is needed by the algorithm. If there are not enough types of data available from the data steward, “low intensity” data types can be requested to fill out the data types requested. These ‘low intensity’ data types are ones that are easily obtained or even regularly collected anyway. The algorithms libraries are altered to call for all the data fields available, thereby rendering reverse engineering extremely difficult, if not impossible.

The embodiment of the invention discloses a data processing method and device, electronic equipment, a storage medium and a product. The method comprises the following steps: inputting attribute information corresponding to scientific research data into a pre-trained watermark generation model to obtain watermark information corresponding to the scientific research data; wherein the watermark generation model comprises generation of a resistance network model; embedding the watermark information into the scientific research data to obtain encrypted data; and performing data processing on the encrypted data based on a data sharing request sent by a data sharing party. According to the technical scheme of the embodiment of the invention, the imperceptibility and robustness of the watermark can be improved, the reverse engineering attack of the model can be resisted, and the data security of the scientific research data during sharing is ensured.

Disclosed are configurations to enable reverse engineering and characterizing machine learning algorithms through controlled data manipulation. A target machine learning system is analyzed by obtaining compatible data, applying data poisoning techniques to induce controlled responses, and generating a unique model signature that quantifies the system's response patterns. The model signature is compared against a codebook of known algorithm signatures to identify the underlying algorithm type. The codebook is built and maintained by applying systematic data manipulations, such as data poisoning techniques, to known machine learning algorithms and recording their characteristic responses. Multiple data poisoning techniques may be applied sequentially, with features extracted from the system's responses assembled into multi-dimensional feature vectors. This approach enables identification and vulnerability assessment of machine learning systems without requiring access to their internal structures or source code, supporting both offensive operations to identify vulnerabilities and defensive operations to enhance robustness.

The invention discloses a generated text detection method and system based on lightweight rewriting conversion and a medium, and the method comprises the steps: carrying out the AI removal conversion of a to-be-detected text through a forward rewriting model, fusing the to-be-detected text and a generated human-like equivalent text, inputting the fused text into a classifier, and outputting a classification result; the training process of the forward rewriting model comprises the following steps: constructing a pairing sample set by utilizing an artificially written text and a machine generated text, and finely tuning the forward rewriting model by minimizing cross entropy loss, the machine generated text is a text which is obtained by generating and filtering redundant samples through at least one large language model in a reverse manner through the large language model; according to the generated text detection method and system and the medium, access to any external, closed source or exclusive large language model API is not needed completely, the cost and delay are reduced, the system architecture and the deployment process are greatly simplified, and the problems that a watermark technology and a similarity regeneration method are limited by a closed source model and are difficult to actually deploy are solved.

The invention discloses a BIM (Building Information Modeling)-based piping shaft casing pipe construction method, which comprises the following steps of: acquiring three-dimensional point cloud data of an internal structure of a piping shaft by utilizing laser scanning equipment, and converting the three-dimensional point cloud data into a BIM model through three-dimensional modeling software; restoring the existing pipeline layout in the BIM model by using a reverse engineering method; optimizing the casing design according to the prediction result, and simulating the installation process in the BIM model; generating a construction guide with an augmented reality mark; displaying superposition of the virtual model and the real environment through AR glasses; after the installation is completed, performing three-dimensional scanning by using laser scanning equipment, and comparing with original data to evaluate the precision; if it is found that the deviation exceeds a preset threshold value, simulating a repairing scheme through a BIM model and converting the repairing scheme into a field executable instruction; and finally, the sleeve is accurately adjusted through a customized fine adjustment tool until the deviation is controlled within a preset range, and repairing is completed. The problem that the pipeline and the wall surface are difficult to repair in a narrow space is solved, and the construction precision and efficiency are improved.

The invention discloses a method and system for determining a product design decision based on information efficiency, and particularly relates to the technical field of product design decisions, and the method comprises the following steps: S1, information source collection and establishment, S2, knowledge graph construction, S3, dynamic information entropy weight distribution, S4, reverse information flow verification, S5, reverse engineering data integration, S6, information fusion processing, S7, decision making, S8, fault-tolerant mechanism, and S9, fault-tolerant mechanism determination. And performing reverse verification. According to the method, the dynamic knowledge graph capable of supporting semantic association retrieval and context reasoning is constructed by determining the multi-dimensional information source, the information weight is dynamically adjusted according to the information generation time and the association degree, inspiration is provided for innovative design, and the decision accuracy is improved; and the decision model can be corrected by using a failure case library, and the knowledge graph logic relationship is corrected through product version test feedback, so that the decision process is optimized, and continuous innovation of the product is promoted.

The invention discloses a quantitative analysis method and system for the productivity loss of a mobile production line. The method comprises the following steps: firstly, collecting the real-time state and production takt time sequence data of production line equipment; the standard beat ICT of each device is calculated based on the beat data, the actual capacity is calculated by comparing the ICT or integrating the available time and the fault and material shortage duration, and the design bottleneck device and the actual bottleneck device are dynamically recognized; taking bottleneck equipment as a core, counting the total duration of various abnormal states, and quantitatively calculating the total capacity loss quantity of a production line; and performing time sequence backward deduction and logic matching on the external exception of the bottleneck equipment, and tracing and positioning to upstream or downstream source equipment and specific exception events which cause the exception. The system comprises a data acquisition module, a processing analysis module and a result output module for realizing the functions. Accurate quantification, dynamic bottleneck identification and root cause automatic tracing of capacity loss are realized, and data-driven decision support is provided for accurate operation and maintenance and efficiency improvement of a production line.

The invention relates to the technical field of artificial intelligence security, in particular to a training-authorization-reasoning three-in-one large language model dynamic disturbance watermark encryption protection system and method, comprising three closely associated stages of model training encryption, model distribution authorization watermark injection and model reasoning verification authorization; the method has the beneficial effects that the encrypted model weight cannot be directly used even if the encrypted model weight is illegally acquired, so that a core research and development result is protected; a substantial protection layer is provided for algorithm patent application, and reverse engineering is prevented; model leakage of an outsourcing development link is prevented; and merger and purchase valuation protection: the encrypted assets show complete technical control capability during full-duty investigation.

The invention provides a key node identification method and device in an industrial control system, equipment and a medium. The key node identification method comprises the steps of performing reverse analysis on firmware codes of control equipment to obtain a control logic configuration file; disassembling the control software firmware code to obtain a software disassembling code; performing code labeling, code slicing and code translation on the software disassembly code to obtain a class C pseudo code; performing code analysis on the class C pseudo code to obtain a software structure configuration file; performing file analysis on the control logic configuration file and the software structure configuration file to obtain a service logic relationship between the control equipment and the control software and a service logic relationship between the control equipment and the field equipment; constructing a business logic map based on the business logic relationship between the control equipment and the control software and the business logic relationship between the control equipment and the field equipment; and identifying a key control link in the service logic map, and carrying out key node positioning on the key control link. Therefore, key nodes are effectively positioned.

The invention relates to a JavaScript code encryption method and device, and belongs to the technical field of software security and code obfuscation, and the method comprises the steps: carrying out static and dynamic analysis on a JavaScript code to obtain a plurality of code blocks, calculating the complexity corresponding to each code block, distributing the code blocks to different virtual machines according to the complexity, and storing the code blocks in the virtual machines; due to the fact that different instruction sets are adopted by different virtual machines, after the code blocks are obfuscated based on the different virtual machines, the complexity and the dynamic nature of codes can be increased, an attacker cannot easily recover code logic through static or dynamic analysis, debugging and reverse engineering are effectively prevented, and the code safety is improved.

The invention discloses a Java decompiling method, device and equipment and a computer readable storage medium. The method comprises the following steps: extracting a class file, a resource file, a configuration file and a static resource by decompressing a Java deployment package; based on a predefined third-party library feature library, utilizing Hash comparison and package name / method signature feature matching to accurately classify the class file into a third-party library or a self-researched code; performing byte code analysis on the self-developed class file to generate structured data, and inputting the structured data into a customized large language model (based on byte code-source code parallel corpus training) to generate an initial source code; the optimization step comprises the steps of correcting errors by a static grammar checker and automatically supplementing annotations by a byte code constant pool; reconstructing a project directory structure based on the package name; a class dependency graph is generated through static call graph analysis; and finally, integrating the initial source code, the dependency graph and the configuration file, automatically generating an interface document, a framework document and a configuration document, and realizing complete reverse engineering of the deployment package.

The invention belongs to the technical field of computer vision, three-dimensional reconstruction, scene perception and algorithm design, and discloses a three-dimensional reconstruction generation method and device for a complex scene. The invention discloses a three-dimensional reconstruction generation method for a complex scene. A transparent and reflecting object detection module, a three-dimensional reconstruction refined generation module and a next scanning site prediction module are included. The invention discloses a three-dimensional scanning device for a complex scene. The three-dimensional scanning device comprises an RGB image acquisition unit and a polarization camera, according to the method, high-quality, high-efficiency and high-autonomy three-dimensional reconstruction of transparent and reflective objects and complex shielding scenes is realized, and the method can be widely applied to the fields of industrial detection, cultural relic protection, reverse engineering and the like.

The invention provides a dynamic structure teaching demonstration method and system based on a detachable non-oil-leaving paper umbrella model, and is applied to the technical field of data processing. According to the method, 3D scanning and manual surveying and mapping are fused to obtain oil-paper umbrella component parameters and power node data, a reverse engineering algorithm is used for restoring a core structure, and a standardized digital file is generated; and converting the archive into a disassembled animation and an assembled map, subdividing a key power area by using a modular splitting algorithm, and establishing an interactive teaching model. Extracting culture and mechanical principle correlation factors to construct a dynamic matrix, and analyzing umbrella rib mechanics and power paths to obtain teaching key points; a multi-dimensional feature matrix is established by combining class scene extraction indexes and teaching grades. Finally, optimization factors are determined by comparing teaching data, a target scheme is constructed according to layered screening influence factors of students, multi-dimensional teaching support information is generated according to the scheme effect and dynamic association information, a traditional structure is accurately restored, and understanding and non-enthusiasm cognition of the students on the dynamic structure are improved.

This invention discloses a heat dissipation management method and system for power tools, relating to the field of heat dissipation management technology. The method includes: receiving production task assignments, performing reverse engineering of tool operating conditions to obtain target operating conditions; obtaining multiple distributed temperature thresholds for multiple temperature control zones; pre-constructing a distributed temperature prediction model to predict multiple production temperature change sequences for the target operating conditions; traversing multiple production temperature change sequences to obtain multiple tool overheating time zone sequences; obtaining a water-cooled heat dissipation architecture for the temperature-controlled power tool; fitting the multiple tool overheating time zone sequences to the water-cooled heat dissipation architecture for coolant circulation analysis; outputting heat dissipation timing control parameters; and controlling the circulation of coolant in the water-cooled heat dissipation architecture. This invention solves the technical problem of low heat dissipation efficiency in existing technologies due to the inability to accurately adjust based on real-time temperature changes of various key components of power tools, thereby improving the heat dissipation efficiency of power tools.

The invention provides a code generation method and device based on a large model, equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the technical field of large models and reverse engineering. The specific implementation scheme of the code generation method comprises the following steps: segmenting a pseudo code of a dynamic link library file to obtain a plurality of pseudo code fragments and at least one segmentation position; wherein the pseudo code is obtained by disassembling and reversely analyzing a dynamic link library file; correcting errors in the pseudo code snippets based on the large model, and generating a plurality of code snippets; and according to the at least one segmentation position, combining the plurality of code snippets to generate a target code, the target code is used for analyzing whether the dynamic link library file operated in the target system is safe or not.

The application provides a hidden PMU reverse engineering general framework based on differential analysis and simulation verification. The framework can efficiently analyze the function and underlying micro-architecture behavior of Intel processor hidden PMU events. By observing the count change of the entire event space during the execution of a carefully designed instruction fragment, the underlying micro-architecture behavior related to these events is inferred. Based on these initial inferences, a logically equivalent simulator is implemented to model these events. Through the differential analysis between the simulator count and the real CPU count, the simulator is gradually optimized to be consistent with the real CPU behavior, enabling us to reveal the micro-architecture features behind the hidden PMU events. The application has wide application prospects in the fields of performance monitoring, micro-architecture research and security detection, not only improving the hardware resource utilization efficiency of performance counters, but also significantly expanding the ability range of processor performance analysis.

Various techniques to detect and prevent reverse engineering of computer programs are disclosed. A program may be used as a key in an asymmetric cryptographic scheme, where modification of the program would destroy its usefulness as a key. An operator may be dispersed among different lines of code, so that unauthorized insertion or removal of lines of code causes the code to fail. Content of memory may be hashed at different times to detect unauthorized memory access. Code may be modified and hashes computed and compared to hashes expected from such modification. A duration of execution may be measured and compared to an expected duration, where deviation from expected may indicated unauthorized modification of code. Variables may be mapped according to a secret agreed logic to thwart eavesdropping. A program may be made labyrinthine and complex, so that its useful flow paths are difficult to discern.

A free curved surface and an analytic curved surface can be generated as intended by a user with a simple operation. A reverse engineering support apparatus includes: a shape attribute setting unit that sets a shape attribute to a polygon included in mesh data; a region specifying unit that specifies a region included in the mesh data based on a shape attribute; a data conversion unit that converts a polygon included in the specified region into surface data based on the shape attribute and converts a polygon not included in the region into surface data of a free curved surface; and an output unit that outputs pieces of the surface data created by the data conversion unit as CAD data.

The present invention relates to the field of network security technology, and discloses a defense method for firmware defects of Internet of Things devices. First, by collecting samples of binary programs, firmware and libraries with symbols, a symbol feature library is established with the help of embedded model vectorization technology; then, based on the symbol feature library, the input firmware program is symbol matched; for the program with matched symbols, automated reverse engineering is performed to obtain a set P of possible parameter injection points in the program; for each element in the set P, a prompt word is generated, and the generated prompt word is used to call LLM to generate protection rules, and finally all the generated protection rules are imported into a rule-based protection system. The present invention automatically and accurately identifies and repairs security defects in the firmware of Internet of Things devices through automated technical means, generates corresponding protection rules, reduces manual intervention, improves protection efficiency and accuracy, and improves the security and stability of the system.