22 results about "Filename extension" patented technology

Ransomware detection accuracy is improved by analyzing patterns of changes in filename extension counts, relative to each other, that occur in a file system over time. The disclosed approach is malware-agnostic and does not rely on recognizing malware extensions or on real-time monitoring of the target file system. Instead, during each successive backup job of the target file system, the disclosed technology counts different types of filename extensions and compares the counts to each other and to corresponding counts taken in earlier backup jobs. Preferably, the anomaly detection analysis uses machine learning to discern a behavior pattern of the file system, which indicates how filename extensions are distributed and how much they change between backup jobs over time.

Ransomware detection accuracy is improved by analyzing patterns of changes in filename extension counts, relative to each other, that occur in a file system over time. The disclosed approach is malware-agnostic and does not rely on recognizing malware extensions or on real-time monitoring of the target file system. Instead, during each successive backup job of the target file system, the disclosed technology counts different types of filename extensions and compares the counts to each other and to corresponding counts taken in earlier backup jobs. Preferably, the anomaly detection analysis uses machine learning to discern a behavior pattern of the file system, which indicates how filename extensions are distributed and how much they change between backup jobs over time.

System for automating job matching comprises a user interface 102 for uploading job descriptions 114 and resumes 116, and displaying ranked results of candidate suitability of each job requirement. A database directory 104 stores the job descriptions and resumes. A document extension module 106 examines the file extensions of the stored documents to verify a file type or format. A processing module 108 comprises a large language model (LLM) 110 for processing and interpreting resume content through machine-readable instructions (e.g. natural language processing). This may include generating embeddings (by tokenising the documents, semantically analysing them, and assessing a textual context) and retrievers for each document, merging retrievers for respective resume and job description into one document, and creating filters for embedding and compressing the merged document. A master retriever and prompt template may be created. The LLM is coupled to vector database 118 for performing similarity searching with an embedding vector. A scoring module 112 (e.g. having rubric-based scoring templates and matching algorithm) scores candidates based on suitability for each job requirement. The scoring module may include computing scores based on relevant experience (112a, fig.1A), projects (112b, fig.1A), qualifications (112c, fig.1A), and skills (112d, fig.1A) in the candidate resumes.

The invention relates to the technical field of computer programming, in particular to a redundant file deleting method and system, electronic equipment and a storage medium. The redundant file deleting method comprises the steps of obtaining a clearing command input by a user, wherein the clearing command comprises a target root directory and a target file extension; starting from the target root directory as a path, traversing the whole item directory of the target item corresponding to the target root directory, searching for all target files of which the extension names are target file extension names, and storing absolute paths of the target files into a file array; obtaining a target file name corresponding to each absolute path in the file array; traversing file contents of all non-target extension name files of which the extension names are not target file extension names in the preset directory array, and judging whether a target file name exists in the file contents or not; if not, taking the target file name as an unreferenced file name and adding the unreferenced file name into an unreferenced array; and outputting all unreferenced file names in the unreferenced array. Redundant files can be effectively deleted, and the project size is reduced.

The invention relates to the technical field of computer software, in particular to a file analysis method and system and computing device.The method comprises the steps that a universal analysis interface is defined, and a corresponding implementation class is configured for a preset file format; identifying and loading a corresponding parser class according to the file extension name; and determining the logic of data extraction and analysis through a pre-configured rule set. Aiming at the compatibility problem of a traditional tool during heterogeneous file processing, the invention provides a unified standardized analysis scheme, so that the barrier of format difference is effectively overcome, and the compatibility and flexibility of a system are remarkably improved.

Ransomware detection accuracy is improved by analyzing patterns of changes in filename extension counts, relative to each other, that occur in a file system over time. The disclosed approach is malware-agnostic and does not rely on recognizing malware extensions or on real-time monitoring of the target file system. Instead, during each successive backup job of the target file system, the disclosed technology counts different types of filename extensions and compares the counts to each other and to corresponding counts taken in earlier backup jobs. Preferably, the anomaly detection analysis uses machine learning to discern a behavior pattern of the file system, which indicates how filename extensions are distributed and how much they change between backup jobs over time.

The invention discloses a document analysis method and system based on an open source component and a storage medium thereof, and relates to the technical field of document processing, and the method comprises the following steps: judging a file format of a received original file through a dual detection mechanism based on a file magic number and a file extension name, and selecting and executing a differentiated analysis process according to the file format, the method comprises the following steps: acquiring structured table data and non-table content data, fusing the two parts of data to form a final document, performing structured optimization on the document subjected to file analysis to generate optimized output content convenient for vectorization segmentation and semantic retrieval, and performing intelligent format detection and differential analysis technologies to obtain an optimized output content. According to the method, a complex table structure and merged cells of a document are precisely restored, a placeholder fusion mechanism and RAG special optimization are adopted, a mixed format integrating a standardized title and keeping structural semantics is output, and vector retrieval relevance and LLM knowledge understanding accuracy are remarkably improved.

The invention is suitable for the technical field of computers, and relates to a language file detection method and device, equipment and a medium. Wherein the language file of the IOS application is identified according to the file name; identifying a code file of the IOS application according to the file extension name; identifying a language key in the code file as a reference language key through a regular expression; and performing anomaly detection on the language file according to the reference language key to obtain a detection result, the anomaly detection including at least one of missing key detection, repeated key detection, null key detection and redundant key detection. The detection efficiency of the language file of the IOS application can be improved.

The present disclosure provides a method, an apparatus, an electronic device, a storage medium and a program product for protecting against malware encryption. The method comprises: receiving normal files and malware samples into a sandbox to obtain multiple blacklists and multiple whitelists of said malware samples; performing an operation on said blacklists and whitelists to obtain a list of unencrypted filename extensions of said malware samples; and performing a filename extension operation on said normal files in accordance with said list of unencrypted filename extensions; and receiving said normal files and said malware samples into said sandbox after the filename extension operation, and outputting said list of unencrypted filename extensions if said normal files are not encrypted by said malware samples. In this way, it is possible to obtain a list of filename extensions regarding the avoidance of malware encryption, and to avoid normal file from being encrypted by malware by adding said extensions after normal filenames, ensuring data security and information security.

The application discloses an electronic official document dynamic self-adaptive encryption method and system based on a quantification model, and relates to the technical field of electronic information security, wherein the method comprises the following steps: designing a file type, an encryption mode enumeration, a file attribute and an encryption strategy structure body, establishing a file extension and type mapping, configuring system parameters and generating a key and a vector; identifying a file type, detecting content repetition characteristics and sensitive attributes, and integrating to generate a file attribute object; dynamically generating an encryption strategy by means of a multi-level priority rule engine in combination with file characteristics; executing file encryption and generating ciphertext according to the strategy, and realizing decryption restoration based on strategy parameters; uniformly releasing dynamic memory, establishing a hierarchical error processing mechanism for authority, memory, keys and data abnormalities, and completing resource recycling and abnormal fault tolerance closed-loop management and control. The application upgrades the encryption strategy through dynamic intelligent decision-making, realizes dynamic balance between safety and performance, and solves the problem that traditional schemes cannot balance safety and efficiency.

Detecting targeted images, as well as variations thereof within computing networks, such as the Internet and the Metaverse. Navigation to predetermined websites and accessing the HTML code to search for image file extensions and, in some instances video file extensions within the listing of URLs. In response to finding URLs with image or video file extensions, the hyperlink is activated to navigate to the webpage containing the associated image or video and the image or video is downloaded for purposes of target image detection analysis. A virtual surveillance drone is deployed within virtual reality computing systems to navigate the entirety of the environment to search for images or, in some instances videos that may be displayed and, more specifically, images that appear to match or resemble the targeted image. The drone is capable of capturing the images and communicating the images to non-virtual computing systems for purposes of target image detection analysis.

A system with a simple, intuitive, efficient interface is described for creating multi-tagged image files and playing back the tags upon demand. The system includes a display for displaying the image, a user interface is adapted to receive user input to create a user-selectable zone around each selected location, a recording device for creating an object associated with each user-selectable zone and a packing device that merges the image, the user-selectable zones and their associated objects into a tagged image file having a unique filename extension indicating that it is a tagged image file, and saving the tagged image. On playback, the image is displayed to the user who may select a user-selectable zone. The object file associated with that zone is played back. The user may also select an option that causes the objects to autoplay in a pre-determined sequence. The user may also delete, edit, or re-record objects.

This invention discloses a method and terminal for detecting automated game scripts. The method includes the following steps: responding to a detection request, traversing file data, classifying the file data according to file extensions and file header features to obtain script files and image files; detecting the script files and image files, and summarizing and organizing the detection results corresponding to each detection type, displaying the organized detection results. This invention performs syntax and logic checks, file association checks, comment specification checks, and redundancy checks on script files, records the detection results, and summarizes and classifies the detection results to facilitate developers in modifying script files based on the detection results, ensuring the quality and reliability of script files, and improving the detection efficiency and maintainability of script files.

The invention relates to the technical field of file icon generation, and discloses a dynamic generation method and device for a file type icon with an extension name, and the method comprises the steps: obtaining file information of a target file, judging whether the target file is a directory file or not according to the file information, obtaining a preset directory icon corresponding to the directory file when the target file is the directory file, and storing the preset directory icon in the directory file; when the target file is a non-directory file, extracting a file extension name of the target file, determining a file type of the target file according to the file extension name, determining a target key value of the file according to the extension name and the type so as to determine whether a corresponding bitmap exists or not, and when the target file does not exist, generating the bitmap of the target file according to the file extension name and the file type. Visibly, the waste of performance resources and memory resources caused by repeated generation of the icons can be avoided in a mode of directly obtaining the directory icons, the icons can be generated according to the file types and the extension names, the generation flexibility of the file icons is improved, and the expandability of the generated file icons and the icon generation efficiency are improved.

The invention discloses a file extension name identification method, device and equipment, and relates to the technical field of file processing. The method comprises the steps of obtaining target file header information of a target file; obtaining a preset file header information set, and determining a file type corresponding to first file header information as the file type of the target file under the condition that the first file header information matched with the target file header information exists in the file header information set; under the condition that the file type of the target file is an office file, determining a document format of the target file according to the header information of the target file; analyzing the target file according to the document format of the target file to obtain directory content features of the target file; and determining an extension name of the target file according to the directory content characteristics. According to the embodiment of the invention, the extension name of the file can be accurately identified.

The invention discloses a processing method and system for bank outgoing data, and relates to the technical field of financial data. When it is determined that the outgoing operation exists in the target equipment, obtaining to-be-sent data from the outgoing operation, and analyzing the to-be-sent data to obtain a data label; if the data tag is the sensitive tag, suspending the outgoing operation and pushing an identity verification request; if the identity information passes verification, user information of the current operator is confirmed; obtaining a file extension name of the to-be-sent data, and processing the to-be-sent data to obtain an initial file type; analyzing the file extension name according to the initial file type to determine a target file type; selecting a corresponding watermark type according to the target file type, processing and embedding the structured text character string into the to-be-sent data through a preset algorithm according to the watermark type, and generating watermark to-be-sent data; and replacing the to-be-sent data with the watermark to-be-sent data, and recovering the suspended outgoing operation. By implementing the technical scheme provided by the invention, the problem that the file type is identified only by depending on the extension name is solved.

A system with a simple, intuitive, efficient interface is described for creating multi-tagged image files and playing back the tags upon demand. The system includes a display for displaying the image, a user interface is adapted to receive user input to create a user-selectable zone around each selected location, a recording device for creating an object associated with each user-selectable zone and a packing device that merges the image, the user-selectable zones and their associated objects into a tagged image file having a unique filename extension indicating that it is a tagged image file, and saving the tagged image. On playback, the image is displayed to the user who may select a user-selectable zone. The object file associated with that zone is played back. The user may also select an option that causes the objects to autoplay in a pre-determined sequence. The user may also delete, edit, or re-record objects.

The file volume processing method, device and server provided by the present disclosure relate to Internet technology, and include the following steps: obtaining and packing static resources of a WEB page according to a packing instruction, generating static resource information, and saving the static resource information in a preset storage space; if it is determined that the packing of the static resources of the WEB page is not the first time, obtaining first and second static resource information from the preset storage space according to a file extension participating in volume change analysis, a file extension not participating in volume change analysis and a file name not participating in volume analysis; and determining volume changes of static resource files in the first static resource information according to the first and second static resource information, so as to eliminate when it is determined that the file volume is too large based on the volume changes. The present scheme can automatically compare the file volumes of the static resources packed this time and last time and display the comparison result, which is efficient and less likely to make mistakes.

A feature vector is created that comprises a plurality of values, each representing a corresponding portion of a filename extension for a digital file. During an inference workflow of a neural network model, an embedding vector is created that represents, in a meaningful way, the feature vector for the filename extension. A class label prediction value is then computed, based on an evaluation of the embedding vector, a first plurality of embedding vectors representing a plurality of feature vectors for a plurality of benign filename extensions, and a second plurality of embedding vectors representing a plurality of feature vectors for a plurality of malicious filename extensions. A prediction as to whether the digital file has been renamed by a malicious computer program is made, based on the class label prediction value.

The application provides a file aggregation identification method and system, and belongs to the technical field of electronic data forensics, and aims to solve the problems of traditional forensics tools relying on file extensions, complicated operation, mixed system analysis confusion and weak non-standard file analysis capability. The core of the method is a four-stage linkage analysis engine: through double checking of file suffix and binary header characteristics, initial screening of file type is realized; through detecting file system identification and verifying system specific path / configuration file, deep identification of image type is realized; through analyzing special metadata structure and storage format, intelligent analysis of backup files is realized; through entropy weighting algorithm, dynamic weight analysis and conflict arbitration are realized. The corresponding system includes four function modules. The application can automatically identify smartphone images, backups and non-standard data files, reduce the misjudgment rate, improve the analysis efficiency by 3.8 times, has strong anti-interference ability, high automation degree and excellent compatibility, and is suitable for complex electronic data forensics scenes.