Abstract: The present disclosure provides techniques for transport mode selection based on MAPC deployment. A network device receives information from one or more other network devices within a CG, where the CG is configured to operate using a MAPC mode, and the information indicates a support of acting as a transport node over wired connections and a MAPC transport role within the CG for each of the other network devices. The network device estimates a wired path between the network device and at least one of the other network device, and determines transit times required for communication over the wired paths and transit times required for communication over wireless paths. Considering the transit times for the wired and wireless paths and the MAPC mode, the network device selects a transport mode, communicates the transport mode to the other network devices, and performs a network coordination operation using the transport mode.

Abstract: There are provided systems and methods for determining processing weights of rule variables for rule processing optimization. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules for decision-making of data including real-time data in production computing environments. Rules may correspond to coded statements that perform an automated decision-making service for the computing services and platforms of the service provider. When writing rules, different variables for data objects may be utilized, where each variable may perform a different operation and/or utilize a different operation for fetching and retrieving data used during rule processing. Each variable may therefore have a different data processing weights based on processing requirements of the data. Thus, optimization of rule authoring may be performed by mapping variables to other similar variables and showing a processing weight of each variable.

Abstract: A computer system includes memory hardware configured to store computer-executable instructions, and a network of multiple tensor networks, each tensor network including multiple nodes. The computer system includes processor hardware configured to execute the computer-executable instructions to transmit a ping request from a first tensor network of the multiple tensor networks to a second tensor network of the multiple tensor networks, contract multiple nodes of the second tensor network in response to the ping request, to generate a probability distribution indicative of a state information of the second tensor network, transmit the probability distribution from the second tensor network to the first tensor network, and update the first tensor network to connect the probability distribution with at least one of multiple nodes of the first tensor network.

Abstract: The current disclosure provides methods and systems that can manage the dynamic-uncertainty induced by resources of decentralized data networks, in order to ensure the stability and sustainability of task-oriented automated operations, such as operations conducted through intelligent agents. In contrast to the state-of-the-art object-based processing, the methods and systems enable object-aware processing, to ensure the establishment of stable and sustainable associations with physical and digital web-objects, while enabling those objects to be processed dynamically with full adaptability in response to contextual and structural alterations in real-time.

Abstract: According to a first aspect there is a method performed by a detector node in a communications network, wherein the detector node is for monitoring messages sent between a first client node, a second client node and a Federated Learning, FL, server, as part of a FL process wherein, as part of the FL process, the first client node sends a local model update to the second client node for forwarding to the FL server. The method comprising: receiving first information relating to the local model update sent from the first client node to the second client node; receiving second information relating to the local model update as received or forwarded by the second client node; and comparing the first information to the second information to determine whether the second client node correctly received or forwarded the first information relating to the local model.

Abstract: Aspects of the subject disclosure may include, for example, obtaining data pertaining to a plurality of user equipment and a plurality of network resources, analyzing the data to identify that a first user equipment included in the plurality of user equipment is a reduced capability device, based on the analyzing, allocating first network resources included in the plurality of network resources to the first user equipment, resulting in a first allocation, and based on the analyzing, allocating second network resources included in the plurality of network resources to a second user equipment included in the plurality of user equipment, resulting in a second allocation. Other embodiments are disclosed.

Abstract: Techniques for managing digital messages to and from a proxy message address are disclosed. A system receives a message directed to a particular destination address. The system replaces any source address included in the message with a proxy address. When the system receives a reply to the message, the reply is directed to the proxy address. The system analyzes message data to identify a target address for the reply message. The system identifies contextual data associated with the reply message. The system transmits the reply message, and the contextual data, to the target address.

Abstract: The present invention relates to the generation of analytics information in a mobile network. To this end, the invention proposes network entities and corresponding methods that support the analytics generation. In particular, the network entities and methods facilitate the gathering of information required for the analytics generation. In particular, a network entity for analytics generation is configured to obtain network slice association (NSA information and/or user plane association (UPA) information from one or more other network entities, wherein the NSA information indicates a relation between an Access Network (AN) property and a Core Network (CN) property, and the UPA information indicates a network entity configured to transmit data traffic to and/or from a user equipment in the network. Further, the network entity is configured to provide analytics information, the analytics information being based on the obtained NSA information and/or UPA information.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using cryptographic techniques to enhance data security and privacy and increase computational efficiency in selecting digital components are described. In one aspect, a method includes receiving, by an MPC computer of a group of MPC computers configured to perform computations of a secure MPC protocol to select digital components for distribution to client devices, a first secret share of location information indicating a location of a client device. The MPC computer generates, by performing the computations of the secure MPC protocol in collaboration with one or more second computers of the group of MPC computers, a first secret share of a selection result including data identifying a selected digital component that is selected from candidate digital components that are candidates based at least in part on the location of the client device.

Abstract: A device includes one or more processors configured to receive an authentication request from a requestor. The one or more processors are also configured to determine, based on content of the authentication request, an ingress mode of the authentication request. The one or more processors are further configured to select, based on the ingress mode, a particular authentication mode from a plurality of authentication modes. The one or more processors are also configured to generate an authentication token based on the particular authentication mode.

Abstract: A system of aggregating servers includes a first aggregating server that establishes a session for linking with a first system; a second aggregating server that establishes a session linking with a second system; wherein the first and second aggregating servers replicate mutually structured data provided by the first and/or second system, to provide access to the structured data of the respective systems for the first and second aggregating servers; and coordinate the respective sessions of the system of aggregating servers with the first and/or second system to prevent improper access to the at least first system to be aggregated and/or the at least second system to be aggregated.

Abstract: Methods and systems for managing operation of a data processing system are disclosed. Hardware resources of the data processing system may issue a command for execution by a channel card of the data processing system. The channel card may be manufactured by a vendor different to a manufacturer of the data processing system. A management controller of the data processing system may identify and screen the command based on vendor software for the channel card (e.g., proprietary software of the vendor). The vendor software may be usable to facilitate use of a non-standard feature of the channel card. When the vendor software indicates that the command implicates use of the non-standard feature, the command may be modified by the management controller. The modified command may be used to initiate execution of the non-standard feature by the channel card to facilitate provisioning of a computer-implemented service.

Abstract: Methods, storage systems and computer program products implement embodiments of the present invention that include initially defining a set of protective actions. Upon detecting, by a security server on a network, an incident including one or more alerts indicating malicious activity by one or more computing devices on the network, extracting a set of features indicating measurable characteristics of the incident are extracted from the alerts. Based on the alerts, respective counts for the features for the detected incident are computed and based on the features and their respective counts, a score indicating a magnitude of malicious activity for the detected incident is computed. A given feature having a highest impact on the score is identified, and a given protective action is selected based on the score, the identified feature and its respective count. Finally, the selected protective action is initiated with respect to at least some of the devices.

Abstract: A machine learning model is trained using information pertaining to accesses of data items at a software-as-a-service (SaaS) management platform. A first training input is generated. The first training input includes access data identifying multiple data items accessed at the SaaS management platform using a subset of user accounts of multiple user accounts associated with a client organization. A first target output is generated. The first target output identifies for each user account, information identifying a respective job title associated with the client organization. The training data is provided to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including the first target output.

Abstract: Techniques for data security transactions using software container machine readable configuration data are described, including receiving at a platform in response to a query, an artifact associated with a software container, parsing the artifact to identify source code of the software container and a supply chain, invoking a scoring engine configured to generate a score associated with a component of the software container and an aggregate score of the software container, the aggregate score being based on a security tool being included with the software container, and generating a report from the platform, the report being associated with the software container and the report including an attestation comprising the score and the aggregate score.

Abstract: A baseline of behavior is received for a set of containers utilized by the Kubernetes manager from a cluster agent. The processes running on the set containers and network traffic generated by the set of containers, to identify anomalous behavior relative to the baseline. Subsequent to the container updates, and responsive to detecting the anomalous behavior exceeds a threshold for a specific container, anomalous data is sent to the cluster agent including an identification and version of the specific container and a description of abnormal behavior. The cluster agent determines a new rule is necessary to define the new behavior and distributes the new rule to the plurality of Kubernetes managers that are affected.

Abstract: A system comprising: at least one training unit for one or more data owners collaborating to the system for storing private data and at least one encrypted model, said training unit being implemented as a trusted execution environment; at least one aggregator unit for each model owner collaborating to the system for storing and executing code of a training algorithm, said aggregator unit being implemented as a trusted execution environment; at least one administration unit for controlling communication and synchronization between the at least one training unit and the at least one aggregator unit, said administration unit being implemented as a trusted execution environment; wherein the communication between the at least one training unit and the at least one aggregator unit is encrypted.

Abstract: A system can generate a risk assessment associated with an identity element used in interactions associated with a user entity. For example, the system can receive historical data related an identity element associated with a user entity, the identity element used in a set of interactions associated with the user entity. The system can generate a binomial distribution of the historical data associated with the identity element. The system can determine, based at least in part on the binomial distribution of the historical data, a risk indicator associated with the identity element. The system can control, based at least in part on the risk indicator associated with the identity element, an interaction involving a target entity and the user entity using the identity element.

Abstract: During operation, a computer system may establish a connection with an electronic device. Then, the computer system may receive, from a port in a firewall, a packet associated with the electronic device, where the packet includes an IP address of the electronic device. Moreover, the computer system may provide, to the port, a response addressed to the IP address. Next, the computer system may receive an acknowledgment associated with the electronic device that indicates that the response was received. Furthermore, the computer system may provide, addressed to the electronic device, a second IP address of a second electronic device and a second port in a second firewall associated with the second electronic device. Additionally, the computer system may provide, addressed to the second electronic device, the IP address of the electronic device and the port in the firewall.

Abstract: A sample file that is associated with malware and a first modification model of a plurality of modification models to alter the sample file are selected. The first modification model generates a modification configuration based on the sample file. The modification configuration identifies a modification to be performed on the sample file. The sample file and the modification configuration are provided to a modification engine to generate a modified sample file. The modification configuration is adjusted based on the first modification model in response to receiving a classification from a classification model that identifies the modified sample file as being free of malware.