Abstract: Aspects of the subject disclosure may include, for example, detecting a need to verify an identity of a particular user in the metaverse, obtaining a particular hash/encoding associated with the particular user, wherein the particular hash/encoding is generated based on processing of a video, image, voice recording, or biometric information associated with the particular user, retrieving a second hash/encoding from a user profile of a first user, wherein the second hash/encoding corresponds to a second user and is generated based on processing of a video, image, voice recording, or biometric information associated with the second user, performing a comparison of the second hash/encoding and the particular hash/encoding, and based on a determination that there is a match, identifying that the particular user corresponds to the second user, resulting in a verification of the identity of the particular user. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining, by a processing system including a processor, an indication of an assertion involving a first avatar of a first user, assessing, by the processing system and based on the obtaining, a validity of the assertion relative to a threshold, and authorizing, by the processing system and based on the assessing, a first action to be taken by the first avatar, a second action to be taken by a second avatar of a second user, or a combination thereof. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method for training a deep learning model that includes encoding a content item; generating a blended image by combining a background image and the encoded content; decoding the blended image to generate decoded content corresponding to the content item; and defining or specifying a loss function related to the deep learning model. The method also includes determining values of training parameters for the deep learning model to minimize the loss function, thereby obtaining a trained deep learning model. The method also includes an information concealing procedure using the trained deep learning model to conceal user content by encoding the user content and blending the encoded user content with a user-selected image; the information concealing procedure is substantially independent of the user-selected image. Other embodiments are disclosed.

Abstract: A method for privacy preserving vulnerability detection for devices. In an example, an apparatus may include a processor and memory coupled with the processor that effectuates operations. The operations may include receiving a type of a device; receiving a feature for the device; creating a first hash value based on the device type and the feature; receiving an indication that the first hash value matches a second hash value; and based on the indication that the first hash value matches the second hash function, sending an alert that the device has a vulnerability.

Abstract: A processing system may obtain a first source data and encoding the first source data in accordance with a first key via an encoder associated with an encoder-decoder model to generate a first source data encoding that is a first output of the encoder and a second key that is a second output of the encoder. The processing system may then transmit the first source data encoding to a recipient system, where the second key is for generating a second source data encoding for transmission by the processing system to the recipient system. A processing system may also obtain a first encoded source data from a sending system, apply the first encoded source data and a first key as inputs to a decoder associated with an encoder-decoder model to obtain a first decoded source data and a second key as outputs, and present the first decoded source data.

Abstract: Aspects of the subject disclosure may include, for example, receiving, from a computing device, a user request to perform a subscriber identity module (SIM) swap for a subscriber, wherein the computing device and a user device are accessible to a user, based on the receiving the user request, generating a code for the SIM swap, resulting in a generated code, providing the generated code to the computing device for presentation, obtaining, from the user device, data associated with the user, the user device, or a combination thereof, facilitating user authentication responsive to the obtaining the data, detecting a transmission of a particular code from the user device, resulting in a detected code, and, based on the detecting the transmission and based on the facilitating the user authentication, performing an action relating to the SIM swap. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving a request to initiate an interactive session involving a first user device and a second user device, initiating the interactive session, performing a split of inputs or outputs by determining a first set of inputs or outputs for the first user device and a second set of inputs or outputs for the second user device, such that first data in the interactive session that is permitted to be outputted only to the first user device is accessible to the first user device but not to the second user device and such that second data that is permitted to be outputted only to the second user device is accessible to the second user device but not to the first user device, and facilitating the interactive session based on the split of inputs or outputs. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of facilitating establishment of a communications path between an open device and a remote device through a communications network; receiving an indication of potentially sensitive information on the communications path; and facilitating establishment of a secure communications path between a secure device and the remote device through the communications network. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, identifying a first sensitivity level associated with a first data item, comparing the first sensitivity level to a first threshold, resulting in a first comparison, selecting a first device based on the first comparison, transmitting the first data item to the first device based on the selecting of the first device, identifying a second sensitivity level associated with a second data item, wherein the second sensitivity level is different from the first sensitivity level, comparing the second sensitivity level to a second threshold, resulting in a second comparison, selecting a second device based on the second comparison, wherein the second device is different from the first device, and transmitting the second data item to the second device based on the selecting of the second device. Other embodiments are disclosed.

Abstract: A method for privacy preserving vulnerability detection for devices. In an example, an apparatus may include a processor and memory coupled with the processor that effectuates operations. The operations may include receiving a type of a device; receiving a feature for the device; creating a first hash value based on the device type and the feature; receiving an indication that the first hash value matches a second hash value; and based on the indication that the first hash value matches the second hash function, sending an alert that the device has a vulnerability.

Abstract: A system allows for higher security guarantees on public or shared or even compromised equipment, which may be designated as untrusted equipment, without a need to install additional software on it or connect to it outside of general browser interactions. Based on this system, even if it is compromised, proper information separation may ensure security of the sensitive parts of data. That is, trusted information will only go to (outputs) or come from (inputs) trusted devices and insensitive information can go to or come from both trusted and untrusted devices.

Abstract: Aspects of the subject disclosure may include, for example, a mobile assistant device that obtains information regarding device locations of a plurality of devices; the mobile assistant device is enabled to move among at least a portion of the device locations. The mobile assistant device detects an indication of a need for maintenance, repair, and/or user attention, relating to a target device. The mobile assistant device moves to a location of the target device; obtains device information regarding a condition of the target device; and reports the device information to equipment of the user and/or a controller of the mobile assistant device. The mobile assistant device engages with the user and/or equipment of the user to provide instructions to the equipment of the user for resolving an issue relating to the target device, and can physically engage with the target device to resolve the issue. Other embodiments are disclosed.

Abstract: An insider attack resistant system for providing cloud services integrity checking is disclosed. In particular, the system utilizes an automated integrity checking script and virtual machines to check the integrity of a service. The system may utilize the integrity checking script and virtual machines to execute a set of operations associated with the service so as to check the integrity of the service. When executing the set of operations, the system may only have access to the minimum level of access to peripherals that is required for each operation in the set of operations to be executed. After each operation is executed, the system may log each result for each operation, and analyze each result to determine if a failure exists for any of the operations. If a failure exists, the system may determine that a change in an expected system behavior associated with the service has occurred.

Abstract: Aspects of the subject disclosure may include, for example, a method for training a deep learning model that includes encoding a content item; generating a blended image by combining a background image and the encoded content; decoding the blended image to generate decoded content corresponding to the content item; and defining or specifying a loss function related to the deep learning model. The method also includes determining values of training parameters for the deep learning model to minimize the loss function, thereby obtaining a trained deep learning model. The method also includes an information concealing procedure using the trained deep learning model to conceal user content by encoding the user content and blending the encoded user content with a user-selected image; the information concealing procedure is substantially independent of the user-selected image. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method for training a deep learning model that includes encoding a content item; generating a blended image by combining a background image and the encoded content; decoding the blended image to generate decoded content corresponding to the content item; and defining or specifying a loss function related to the deep learning model. The method also includes determining values of training parameters for the deep learning model to minimize the loss function, thereby obtaining a trained deep learning model. The method also includes an information concealing procedure using the trained deep learning model to conceal user content by encoding the user content and blending the encoded user content with a user-selected image; the information concealing procedure is substantially independent of the user-selected image. Other embodiments are disclosed.

Abstract: In one example, the present disclosure describes various methods, computer-readable media, and apparatuses for supporting social engineering attack prevention based on early detection and remediation of various types of social engineering attacks which may be initiated within various contexts. In one example, supporting social engineering attack prevention may include identifying a workflow to be protected, identifying, for the workflow, a set of valid resources of the workflow where the set of valid resources includes a set of artifacts and a set of templates, identifying, from a dataset associated with the workflow and based on the set of artifacts, a communication associated with the workflow, determining, based on an analysis of the communication based on the set of templates, that the communication is malicious, and initiating, based on the determination that the communication is malicious, a remediation action.

Abstract: Aspects of the subject disclosure may include, for example, a method for training a deep learning model that includes encoding a content item; generating a blended image by combining a background image and the encoded content; decoding the blended image to generate decoded content corresponding to the content item; and defining or specifying a loss function related to the deep learning model. The method also includes determining values of training parameters for the deep learning model to minimize the loss function, thereby obtaining a trained deep learning model. The method also includes an information concealing procedure using the trained deep learning model to conceal user content by encoding the user content and blending the encoded user content with a user-selected image; the information concealing procedure is substantially independent of the user-selected image. Other embodiments are disclosed.

Abstract: A secure connection is facilitated between a device and a network. A security buffer device is used to determine an available network and connect to the network. The security buffer device can then allow the device to connect to the network via the security buffer device. The security buffer device can monitor any security breaches from the network and perform an action based on the indication of a security breach.

Abstract: An insider attack resistant system for providing cloud services integrity checking is disclosed. In particular, the system utilizes an automated integrity checking script and virtual machines to check the integrity of a service. The system may utilize the integrity checking script and virtual machines to execute a set of operations associated with the service so as to check the integrity of the service. When executing the set of operations, the system may only have access to the minimum level of access to peripherals that is required for each operation in the set of operations to be executed. After each operation is executed, the system may log each result for each operation, and analyze each result to determine if a failure exists for any of the operations. If a failure exists, the system may determine that a change in an expected system behavior associated with the service has occurred.

Abstract: An insider attack resistant system for providing cloud services integrity checking is disclosed. In particular, the system utilizes an automated integrity checking script and virtual machines to check the integrity of a service. The system may utilize the integrity checking script and virtual machines to execute a set of operations associated with the service so as to check the integrity of the service. When executing the set of operations, the system may only have access to the minimum level of access to peripherals that is required for each operation in the set of operations to be executed. After each operation is executed, the system may log each result for each operation, and analyze each result to determine if a failure exists for any of the operations. If a failure exists, the system may determine that a change in an expected system behavior associated with the service has occurred.