10 results about "Comma-Separated Values" patented technology

An automated method and system for modeling intrusion detection on an Internet of Things network preprocess received raw data packets. The raw data packets are converted to a packet capture file, which is converted to a comma separated value (CSV) file. The CSV file is filtered to create normal and abnormal CSV files. An attack is identified in the abnormal CSV file. Features associated with the attack are labeled and classified as an attack type. The abnormal and normal CSV files are merged. Redundant data is removed from the merged file, which is added to a concatenated file with other merged files. Features are extracted from the concatenated file and a machine language (ML) model is built using the extracted features. ML classifiers are determined for each merged file for the ML model to build an ensemble ML model. A dynamic ensemble of ML classifiers detects evolving attack types.

The invention discloses a register model and register transfer level generation and test method, device, equipment and medium, and relates to the technical field of integrated circuit design verification, and the method comprises the following steps: analyzing a comma separation value table file by using a register generation platform to generate a register model, a register transfer level and a register test vector; the comma separation value table file is a file used for describing register information; generating a general verification methodology environment by using a test environment generation platform, and integrating the general verification methodology environment based on the register model, the register transfer level and the register test vector to obtain a corresponding integrated test environment; and compiling the integrated test environment, if the compiling is successful, running the register test vector in the integrated test environment which is successfully compiled, and outputting a corresponding test result. Therefore, the automation degree of register model verification can be improved, and the verification efficiency of front-end verification is improved.

The model generation platform enables generation of machine learning models based on modular storage of user-specified training data. The platform can obtain a dataset from a different heterogenous source (e.g., a structured database, an unstructured database, a semi-structured file system, manual upload of a comma-separated value file, a spreadsheet, and / or big data) through an associated application programming interface and store this data in a first storage medium. The model generation platform can obtain an indication of a portion of the dataset from a user via a user interface and determine a second storage medium for this portion of the dataset based on an associated estimated performance metric. In response to a request for generation of a machine learning model, the model generation platform can generate a machine learning model using training data comprising a subset of the portion of the dataset.

A system and method for ingesting data is disclosed. In example embodiments, the method may include receiving a structured data file, such as a comma separated value (CSV) file, and translating the file to an internal schema. Translating the file to the internal schema may including mapping one or more incoming fields of the file to one or more internal fields of the internal schema. The method may further include displaying a user interface and receiving, via the user interface, a mapping of an unmapped incoming field to a selected internal field. The method may further include receiving a second structured data file, which may include a second plurality of incoming fields, including the unmapped field. The method may further include mapping the unmapped field of the second file to the selected internal field.

An automated method and system for modeling intrusion detection on an Internet of Things network preprocess received raw data packets. The raw data packets are converted to a packet capture file, which is converted to a comma separated value (CSV) file. The CSV file is filtered to create normal and abnormal CSV files. An attack is identified in the abnormal CSV file. Features associated with the attack are labeled and classified as an attack type. The abnormal and normal CSV files are merged. Redundant data is removed from the merged file, which is added to a concatenated file with other merged files. Features are extracted from the concatenated file and a machine language (ML) model is built using the extracted features. ML classifiers are determined for each merged file for the ML model to build an ensemble ML model. A dynamic ensemble of ML classifiers detects evolving attack types.

The model generation platform enables generation of machine learning models based on modular storage of user-specified training data. The platform can obtain a dataset from a different heterogenous source (e.g., a structured database, an unstructured database, a semi-structured file system, manual upload of a comma-separated value file, a spreadsheet, and / or big data) through an associated application programming interface and store this data in a first storage medium. The model generation platform can obtain an indication of a portion of the dataset from a user via a user interface and determine a second storage medium for this portion of the dataset based on an associated estimated performance metric. In response to a request for generation of a machine learning model, the model generation platform can generate a machine learning model using training data comprising a subset of the portion of the dataset.

A method includes acquiring network monitoring and analysis (NMA) alarm processing rules residing in flat format data files, transforming the flat format data files into comma-separated values (CSV) files, clustering the CSV files into a plurality of clusters each containing a subset of the CSV files, for a selected cluster, aggregating the subset contained within the selected cluster, so that the subset is transformed into a smaller subset, applying at least one pre-processing technique to the smaller subset, converting the smaller subset of CSV files into JavaScript object notation (JSON) files, executing a generative artificial intelligence technique that takes as an input one of the JSON files and an example network service assurance system (NSAS) policy engine framework (PEF) policy and outputs a code for converting any JSON file into a NSAS PEF policy, and transforming, using the code, the JSON files into NSAS PEF policies.

The technical problem solved by the present application is: how to automatically, accurately and verifiably convert the business test intention described by product managers or testers in natural language (such as Excel spreadsheets or comma-separated value files (CSV, Comma-Separated Values)) into executable end-to-end test code that meets project specifications without increasing the coding burden of test engineers. In order to solve the above technical problem, the first aspect of the technical solution of the present application discloses an end-to-end automated test code generation system based on test intermediate representation and AI self-correction closed loop. The second aspect of the technical solution of the present application discloses an end-to-end automated test code generation method based on test intermediate representation and AI self-correction closed loop.

The invention discloses a data backup method and device, electronic equipment and a readable storage medium, which are used for solving the technical problems that the data backup process is difficult to monitor and manage, the occupied storage space is large, and an effective compression mechanism is lacked. The method comprises the steps that when a to-be-executed task created by a user for identity authentication service data is detected in a preset database, the backup type of the to-be-executed task is determined; determining a database table and a file directory of the to-be-executed task according to the backup type; when the backup type is a person-certificate record, exporting the database table as a first comma separation value file by using a COPY command of a relational database management system; compressing the first comma separation value file by using a zstd compression algorithm to obtain first backup data; and backing up the file directory by using a command line program to obtain second backup data.

A method includes acquiring network monitoring and analysis (NMA) alarm processing rules residing in flat format data files, transforming the flat format data files into comma-separated values (CSV) files, clustering the CSV files into a plurality of clusters each containing a subset of the CSV files, for a selected cluster, aggregating the subset contained within the selected cluster, so that the subset is transformed into a smaller subset, applying at least one pre-processing technique to the smaller subset, converting the smaller subset of CSV files into JavaScript object notation (JSON) files, executing a generative artificial intelligence technique that takes as an input one of the JSON files and an example network service assurance system (NSAS) policy engine framework (PEF) policy and outputs a code for converting any JSON file into a NSAS PEF policy, and transforming, using the code, the JSON files into NSAS PEF policies.