11 results about "Torrent file" patented technology

The invention discloses a game updating processing method. The game updating processing method comprises the following steps: S10, setting a game updating system; s20, issuing the resources; s30, the Torrent file is updated: based on the latest Torrent, the internet bar server sends a connection request to the Tracker server, and a downloading channel of the resource is obtained; in the initial stage, the Tracker server returns an address of the OMC server, and the Internet bar server obtains Torrent information from the OMC server; after part of the internet bar servers finish the Torrent downloading, when other internet bar servers continue to request the Torrent file, the Tracker server provides the address of the internet bar server finishing the Torrent downloading, and obtains the Torrent file from the internet bar server finishing the downloading; and S40, downloading resource data: after the Internet bar server completes the downloading of the Torrent file, obtaining the resource information of the Torrent file by analyzing the file, calculating the difference information, completing the difference comparison by the Internet bar server, determining the slice needing to be downloaded, connecting to the Tracker server again, obtaining the Peers list, and completing the resource downloading.

The application discloses a kind of fuzzy test method and process based on deep learning model.First, different formats of Linux application are collected, and the application collected and seed file with the same format are saved;Second, the variation model under fixed variation strategy is constructed based on value-oriented DQN algorithm and strategy-oriented TRPO algorithm, and the improved Thompson sampling algorithm is used to construct Havoc random variation model, to output the effective variation operator and variation strategy chain information of test sample;Then, the AFL source code is modified, the two proposed variation models are embedded, and the interaction between the model and AFL is realized using shared memory;Finally, for the AFL embedded with the model obtained in the last step, an automated execution script is written and its execution time is set, and the afl-fuzz, afl-showmap and afl-cov instructions are executed to train the embedded two models and perform automated fuzz testing, and according to the fuzz testing execution result, the code coverage, the number of crashes, the use of variation operator and other results are visualized.

The invention provides a fuzzy test method and system for a command line of a multi-architecture operating system. The method comprises the following steps: S1, receiving a command line example or a historical seed file and a test parameter input by a user through a graphical interface; s2, automatically identifying a current system hardware architecture and deploying a corresponding fuzzy test tool supporting a QEMU mode and a dependency environment; s3, automatically generating a test seed file based on user input, and calling a fuzzy test tool to execute a test in a QEMU mode; and S4, automatically collecting test results and generating a visual report. According to the method, through full-process imaging and automatic design, the technical threshold of fuzzy testing is remarkably reduced, unified testing of multiple architecture platforms such as X86, ARM and Loongson is supported, a complete testing closed loop from seed generation to result analysis is formed, and the testing efficiency and the vulnerability mining capacity are effectively improved.

The invention relates to a fuzzy testing method and device, equipment, a medium and a model training method. The method comprises the steps that a seed file is obtained based on a trained seed generation model, fuzzy testing is conducted on a target application through the seed file to obtain a testing result of the target application, and the seed generation model is obtained by training an initial generation model through a target loss function based on a judgment result; the judgment result is a result obtained by calling an initial generation model to process the random noise file to obtain a simulation seed file and calling a target judgment model to judge the simulation seed file, and the target judgment model is used for judging the probability that the input seed file belongs to a real seed file of the target application. By adopting the method, the seed generation model can learn the complex data distribution and the internal grammar structure of the real seed file of the target application through the antagonism training of the seed generation model and the target discrimination model, so that the test efficiency and the vulnerability discovery capability are enhanced.

The application provides a decentralized downloading method for sharing large files, which comprises the following steps: deploying a Transmission software in a server and each user terminal, generating a seed file for a to-be-downloaded file by using the software in the server; virtually dividing the to-be-downloaded file into virtual blocks with equal sizes, and writing index information and Hash verification codes of each virtual block into the seed file; accessing the server by the user terminal, and parsing the seed file by using the Transmission software of each user terminal to obtain the index information and Hash verification codes, download corresponding file segments from the to-be-downloaded file, and generate seed segment files for the file segments; and accessing other user terminals by the user terminal, parsing the seed segment files of the other user terminals by using the Transmission software of the user terminal, and downloading corresponding file segments. The application expands the uploaders from only the server to the user terminals, that is, the user terminals are downloaders and uploaders at the same time, and realizes the decentralization of downloading.

A system may perform operations including, for a given seed file, obtaining a first graph query to extract a first set of feature values from a first dataset, extracting, based on a feature calculation time, a sub-graph from the first dataset, obtaining, based on a second logic, a second graph query to extract a second set of feature values from a second dataset, and calculating a parity between the first set of feature values and second set of feature values. The operations may also include obtaining the events from a first data store, the events being filtered based on the given seed file, replaying the events backwards and extracting a third set of feature values, determining whether any of the second set of feature values do not match the second set of feature values, and updating the second set of feature values based on third set of feature values.

This disclosure provides an object download method. The method includes: A first client sends a first seed obtaining request to an object storage service on a first server; the first server sends a seed file of a target object to the first client based on the first seed obtaining request; the first client sends a first multi-source download provider obtaining request to a second server based on the seed file, where the first multi-source download provider obtaining request is used to request to provide a list of at least one client that downloads the target object; the second server sends a first multi-source download provider list to the first client based on the first multi-source download provider obtaining request; and the first client receives a first part of the target object from the first server, and receives a second part of the target object from the second client.

The application discloses a kind of redundancy file identification method, device, equipment, medium and computer program product, the method includes: obtaining seed file and the metadata information and file label of each file in file library specified by user;According to file label and file type, file is divided into corresponding fine classification set;For seed file and file in file library, respectively extract semantic feature vector and metadata feature vector and carry out multimodal fusion, obtain the comprehensive feature vector of each file;The similarity between the comprehensive feature vector of seed file and other files in the same fine classification set is calculated;The file with similarity greater than preset threshold is filtered as candidate redundant file, and a redundant file list is generated.The application fully utilizes different modal information to identify similar redundant files with the seed file selected by the user by fusing metadata information, picture encoding information and text encoding information, thereby effectively improving the identification accuracy and efficiency of redundant files.

A machine may form all or part of a network-based system configured to provide media service to one or more user devices. The machine may be configured to define a station library within a larger collection of media files. In particular, the machine may access metadata that describes a seed that forms the basis on which the station library is to be defined. The machine may determine a genre composition for the station library based on the metadata. The machine may generate a list of media files from the metadata based on a relevance of each media file to the station library. The machine may determine the relevance of each media file based on a similarity of the media file to the genre composition of the station library as well as a comparison of metadata describing the media file to the accessed metadata that describes the seed.

A system may perform operations including, for a given seed file, obtaining a first graph query to extract a first set of feature values from a first dataset, extracting, based on a feature calculation time, a sub-graph from the first dataset, obtaining, based on a second logic, a second graph query to extract a second set of feature values from a second dataset, and calculating a parity between the first set of feature values and second set of feature values. The operations may also include obtaining the events from a first data store, the events being filtered based on the given seed file, replaying the events backwards and extracting a third set of feature values, determining whether any of the second set of feature values do not match the second set of feature values, and updating the second set of feature values based on third set of feature values.

The invention discloses a genetic algorithm-based directional fuzzy test seed file variation method and device, and belongs to the technical field of software security. The method comprises the steps of obtaining a seed file used for generating a test case for performing a directional fuzzy test; generating a plurality of probability distribution vectors corresponding to the seed file; constructing a plurality of chromosome clusters, and determining all optimal chromosomes corresponding to the seed file by using a genetic algorithm; determining a global optimal chromosome based on all the optimal chromosomes, determining mutated bytes in the seed file based on the global optimal chromosome, and mutating the seed file to generate a test case. According to the invention, the quality of the generated test case is improved on the whole.