Abstract: Techniques for implementing cross-domain identity trust using trusted token issuers are described. An identity provider is registered with an identity service of a cloud provider network in association with an organization or user. Thereafter, tokens generated by the identity provider can be provided to the identity service as part of token exchanges to obtain new tokens useful for accessing resources managed or provided by applications, such as services, within the cloud provider network. The token exchange process can include use of administrator-provided mapping information indicating how to associate user identity information from an identity provider token with user identity information in a directory utilized by the identity service, whereby the new tokens include identity information that is easily utilized by receiving applications for making access control determinations and performing logging for auditability.

Abstract: Various embodiments include systems and methods for generating a prompt for a large generative AI model (LXM). A computing device may be configured to receive a user prompt, process the received user prompt to recognize whether the prompt includes privacy information or will cause an LXM to provide a response that will reveal privacy information; and use the LXM to provide a response to the user prompt in a manner that will avoid disclosure of privacy information.

Abstract: The devices, systems, and methods described herein facilitate the detection of tampering with RFID scan information associated with an item carried by a cargo transport platform. In some examples, an RFID reader scans a cargo area of the cargo transport platform to detect RFID tags that are each associated with a particular item carried by the cargo transport platform. The RFID scan information generated by the RFID reader is recorded in a database. If any of the records in the database were tampered with after being recorded, the system uses blockchain technology to enable interested parties to detect the tampering and identify which records were modified, added, or deleted.

Abstract: Techniques are disclosed for enforcing isolation in a cluster of computing nodes configured for executing containerized applications. The system receives a request from a requesting entity for access to a target namespace. The request is accompanied by a token. Based on the token, the system identifies a namespace that corresponds to an isolation namespace. To determine if the request is attempting to breach isolation, the system compares the target namespace to the corresponding namespace. If the target namespace is not the corresponding namespace, the system concludes that the request is attempting to breach isolation, and, therefore, denies the request. If the request is not attempting to breach isolation, the system determines if the request is allowed by any permissions that have been granted to the requesting entity. If the request is not allowed by a permission granted to the request entity, the system denies the request.

Abstract: A system and method for utilizing a generative adversarial model for training in a cybersecurity range is presented. The method includes: generating a plurality of scenarios based on a plurality of predetermined actions in a cloud computing environment; initiating a cybersecurity range, the cybersecurity range including a secure cloud computing environment and a plurality of workloads deployed therein; generating a prompt for a large language model (LLM) based on a template, a scenario of the plurality of scenarios, and an identifier of a workload of the plurality of workloads; and initiating an action in the cybersecurity range based on an output generated by the LLM in response to the prompt.

Abstract: A data processing method includes invoking an object login service based on an authorization request transmitted through an application client, obtaining target login information entered in the object login service, performing matching of the target login information with object credential information through the object login service, obtaining an object authorization code if the target login information matches the object credential information, receiving a token obtaining request transmitted by the application client based on the object authorization code, invoking a token issuance service based on the token obtaining request, and obtaining an object token through the token issuance service. The object token is configured to provide the application client with an operation permission to operate resource data within a scope of authority through the service node.

Abstract: Embodiments include methods performed by a time-sensitive network (TSN) node configured for frame replication and elimination for reliability (FRER). Such methods include receiving a plurality of member streams via a corresponding plurality (n) of disjoint paths in the TSN. Each member stream includes a replication of packets comprising a stream. Such methods include performing the following diagnostics on the received member streams: a first group of diagnostics related to member stream failures, a second group of diagnostics related to packets received out-of-window, a third group of diagnostics related to stream outages, and a fourth group of diagnostics related to recovery timeouts for the TSN node. Other embodiments include TSN nodes configured to perform such methods.

Abstract: Example solutions protect a continuous integration/continuous deployment (CI/CD) pipeline. Examples collect data from a CI/CD pipeline execution data source and/or a CI/CD pipeline task data source. Based on the collected data, a feature group comprising a plurality of records is created. Each record in the feature group represents an execution of the CI/CD pipeline. An anomaly score is generated, using a model representing historical feature groups, for the feature group representing the execution of the CI/CD pipeline. If the anomaly score is above a threshold, an alert is generated to indicate that the collected data represents an anomalous activity.

Abstract: A server management framework based on a reverse connection protocol according to various embodiments of the present disclosure may include: a user device; an agent installed in a server; and an integrated console, wherein the agent may be configured to request communication connection to the integrated console by a reverse connection protocol according to a command received from the integrated console, the integrated console may be configured to receive an access request to the server from the user device, and transmit an address of a connection socket to the user device and the agent, and the agent may be configured to create a shell, and request communication connection for the address of the connection socket, in response to receiving the address of the connection socket from the integrated console, and the user device and the server may be configured to be communication-connected through the connection socket. Various other embodiments are possible.

Abstract: The disclosed technology is directed towards managing the establishment and maintenance of communication connectivity between a device and service. Condition data can indicate when to initiate a state change of a connection between a device and a server. Current device conditions associated with the device are evaluated to determine whether to initiate a state change, e.g., from a connected state to a paused state, or vice-versa. The connectivity may be managed per connection type, e.g., cellular versus Wi-Fi, and on a per-application basis, e.g., turned on for a social media application, off for mail and messaging applications. Settings can be used to manage connectivity for an application, e.g., sometimes connected, sometimes paused based on information specific to that application. Trends, user practices and schedule data can be used to predict and manage connectivity, as well as to notify the user to take some action.

Abstract: An automated system design apparatus provided with configuration proposal generator generates a configuration proposal for a system concretized from abstract requirements for the system; facet model generator concretizes a model relating to a feature, the feature being required in the system from a facet other than a configuration of the system; and evaluator generates information required for evaluation of the configuration proposal based on the concretized model, and for evaluating the configuration proposal based on the information.

Abstract: Methods, systems, and devices for data management are described. A data management system (DMS) may maintain a malware threat feed that includes updated (e.g., periodically or continuously updated) malware intelligence. For example, the malware intelligence may involve YARA rules and known hashes indicative of malware. The DMS may use the malware intelligence to scan each new snapshot of a customer computing system to determine if the new snapshot contains malware. The DMS may periodically, or in response to reception of new malware intelligence, check the files within the most recent snapshot of each computing system for a customer to determine whether any computing system of the customer contains malware. When malware is detected in a file within a snapshot—either based on checking a newly captured snapshot or when checking snapshots based on new malware intelligence—the DMS may determine the temporally first snapshot which contained the corrupted file.

Abstract: The present disclosure provides a system and a method for generating a secure digital certificate for a user using cryptographic hashing and storing the digital certificate on a smart contract by a decentralized blockchain. The method includes (i) generating a registration token for a first user using the cryptographic hashing, (ii) storing the registration token of the first user as a second hash in the smart contract, (iii) receiving digital request through the first user device, (iv) validating (a) the digital request of the first user by (m) authenticating whether the biometrics of the first user, and (n) checking whether the second hash exists in the decentralized blockchain, and (b) the second user associated with a second user device using a driver algorithm, (v) sending a message to the second user device to initiate the digital request and (vi) generating the digital certificate when the first user receives a verification.

Abstract: Disclosed herein are various approaches for supporting active-active replication in a blockchain table having a primary key constraint. A blockchain database object may be stored in a first database, the blockchain database object comprising a field defined as a primary key for the database object and a visibility field. A first row may be inserted in the database object, the first row comprising a particular value for the field designated as the primary key. A primary key value conflict may be detected between the first row and a second row from a second database, the second row comprising the particular value for the field defined as the primary key. A conflict resolution procedure may be implemented to determine that the first row is a winning row of the primary key value conflict between the first row and the second row. A visibility field may be stored within the second row to indicate that the second row does not represent a valid row with respect to the primary key.

Abstract: Some embodiments provide a method for configuring a network device based on configuration fragments. A set of commands for configuring a configuration fragment of a network device is received. The configuration fragment defines a portion of a configuration of the network device. It is determined whether the set of commands are allowed to be applied to the configuration fragment of the network device. Upon determining that the set of commands are allowed to be applied to the configuration fragment of the network device, the portion of the configuration of the network device is configured based on the set of commands.

Abstract: There is provided a computer implemented method of training a detection machine learning model (ML) for identifying a specific deepfake tool of a plurality of deepfake tools used to create a deepfake video, comprising: feeding a plurality of sample authentic videos into the specific deepfake tool, obtaining a plurality of deepfake videos as an outcome of the specific deepfake tool, creating a training dataset comprising a plurality of records, wherein a record includes a deepfake video labelled with a ground truth indicating the specific deepfake tool used to create the deepfake video, and training the detection ML model on the training dataset for detecting that the specific deepfake tool was used to create an input deepfake video.

Abstract: Computer-implemented systems and methods are disclosed, including systems and methods utilizing language models for searching a large corpus of data. A computer-implemented method may include: receiving a first user input comprising a natural language query; vectorizing the first user input into a query vector; executing, using the query vector, a similarity search in a document search model to identify one or more similar document portions, where the document search model includes a plurality of vectors corresponding to a plurality of portions of a set of documents; generating a first prompt for a large language model (“LLM”), the first prompt including at least the first user input, and the one or more similar document portions; transmitting the first prompt to the LLM; receiving a first output from the LLM in response to the first prompt; and providing, via a user interface, the first output from the LLM.

Abstract: A computer-implemented method of using a multi-level (ML) data chain protocol to embed a data chain on a core blockchain, comprising: obtaining one or more ML transactions, wherein each ML transaction comprises one or more carrier pairs, each carrier pair comprising an input and an output, wherein each output comprises data associated with the data chain, and wherein each input comprises a signature that signs the carrier pair; generating a first ML block of the ML data chain, wherein the first ML block is a core blockchain transaction and comprises the respective carrier pairs of the obtained one or more ML transactions, wherein for each carrier pair, a respective position index of the respective input corresponds to a respective position index of the respective output, and b) a first chain output, wherein the first chain output is for being spent by a respective chain input of a subsequent ML block.

Abstract: Systems, computer program products, and methods are described herein for encrypting user device resource transactions. The present disclosure is configured to transmit user data to a neural network, wherein the user data includes gesture data, and wherein the neural network sequences the user data in a unique sequence. Further, the present disclosure is configured to encrypt the user data to create encrypted user data, wherein the encrypted user data includes encrypted gesture data, and wherein encrypting the user data comprises a lattice-based multivariate polynomial encryption. Further, the present disclosure is configured to store the encrypted user data in a blockchain. Further, the present disclosure is configured to validate the blockchain with a consensus algorithm.

Abstract: Embodiments of the invention are directed to systems, methods, and devices for securely performing federated tasks (e.g., the generation and utilizing of machine-learning models). A secure platform computer may operate a secure memory space. Entities participating in a federated project may transmit respective portions of project data defining the federated project. Each entity may provide their respective (encrypted) data sets for the project that in turn can be used to generate a machine-learning model in accordance with the project data. The machine-learning model may be stored in the secure memory space and accessed through an interface provided by the secure platform computer Utilizing the techniques discussed herein, a machine-learning models may be generated and access to these models may be restricted while protect each participant's data set from being exposed to the other project participants.