Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

Methods, computer program products, and systems are presented. The methods include, for instance: analyzing a dataset associated with a service provided by the data protection service provider in order to determine a policy for when and how to replicate the respective components of the dataset corresponding to the service from a source site to a target site, such that the target site may perform the service with a minimum cost.

Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

A method, system, or apparatus to debug software that is reorganized in memory is presented. An interactive debugging session is established with an executable code component corresponding to a packed binary file includes machine code that corresponds to blocks of original source code. A randomly reorganized layout of the machine code is generated in memory based on a transformation defined in a function randomization library. An in-memory object file is created by using a debug data component corresponding to the packed binary file. The debug data component includes symbol table information to debug the blocks of the original source code generated prior to the randomly reorganized layout. The symbol table information is updated based on the randomly reorganized layout of the machine code, and the debugger program is instructed to load the in-memory object file with the updated symbol information to debug the blocks of the original source code.

Systems and methods for censoring text characters in text-based data are provided. In some embodiments, an artificial intelligence system may be configured to receive text-based data and store the text-based data in a database. The artificial intelligence system may be configured to receive a list of target pattern types identifying sensitive data and receive censorship rules for the target pattern types determining target pattern types requiring censorship. The artificial intelligence system may be configured to assemble a computer-based model related to a received target pattern type in the list of target pattern types. The artificial intelligence system may be configured to use a computer-based model to identify a target data pattern corresponding to the received target pattern type within the text-based data, identify target characters within the target data pattern, and to assign an identification token to the target characters.

Techniques are disclosed for modularly creating a virtual representation of physical infrastructure of a computing environment. For example, a method comprises generating a virtual representation of physical components of a computing environment. The virtual representation is generated by enabling selection of templates from a pre-stored template database wherein the templates respectively represent the physical components, and by integrating the selected templates with one another to collectively represent the physical components. The method then manages the physical components via the virtual representation.

A system for returning synthetic database query results. The system may include a memory unit for storing instructions, and a processor configured to execute the instructions to perform operations comprising: receiving a query input by a user at a user interface; determining, based on natural language processing, a type of the query input; determining, based on the received query input and a database language interpreter, an output data format; returning, based on a generation model and the output data format, a result of the query input; providing, to a plurality of training models and based on the determined query type, the query input and the result; and training the training models, based on the query input and the result.

A cloud computing system can be configured to generate data models. A model optimizer of the cloud computing system can provision computing resources of the cloud computing system with a data model. A dataset generator of the cloud computing system can generate a synthetic dataset for training the data model. The computing resources can train the data model using the synthetic dataset. The model optimizer can store the data model and metadata of the data model in a model storage. The cloud computing system can receive production data from a data source by a production instance of the cloud computing system using a common file system. The production data can be processed using the data model by the production instance. The computing resources, the dataset generator, and the model optimizer can be hosted by separate virtual computing instances of the cloud computing system.

A system for returning synthetic database query results. The system may include a memory unit for storing instructions, and a processor configured to execute the instructions to perform operations comprising: receiving a query input by a user at a user interface; determining, based on natural language processing, a type of the query input; determining, based on the received query input and a database language interpreter, an output data format; returning, based on a generation model and the output data format, a result of the query input; providing, to a plurality of training models and based on the determined query type, the query input and the result; and training the training models, based on the query input and the result.

A method translates the native machine codes that do not allocate memory for metadata, do not store, and do not propagate metadata by augmenting them with extra instructions to allocate memory for metadata, to store, and to populate metadata such that metadata are readily available at run time for checking programming errors.