Abstract: One example method includes extracting an expected communication specification of a service that is in development in a build pipeline, stimulating the service to exercise communication variations of the service, capturing communication traffic involving the service and one or more endpoints, comparing the captured communication traffic to the expected communication specification, and based on the comparing, detecting, in the captured communication traffic, and flagging, any anomalous communication behavior of the service.

Abstract: Techniques for training data protection in an artificial intelligence model execution environment are disclosed. For example, a method comprises executing a first portion of an artificial intelligence model within a trusted execution area of an information processing system and a second portion of the artificial intelligence model within an untrusted execution area of the information processing system, wherein data at least one of obtained and processed in the first portion of the artificial intelligence model is inaccessible to the second portion of the artificial intelligence model. Data obtained in the trusted execution area may comprise one or more data samples in an encrypted form usable to train the artificial intelligence model.

Abstract: Techniques for integrating a trusted execution platform with a function-based service framework are disclosed. For example, a method obtains an application program comprising a first set of one or more functions for execution within a secure execution area of a function-based service framework and a second set of one or more functions for execution within a non-secure execution area of the function-based service framework. A client attests an attestation delegator and the attestation delegator attests one or more secure containers prior to receipt of a function execution request to execute a function in the function-based service framework.

Abstract: A computer-implemented method comprising: receiving, by a first radio frequency (RF) receiving device, an RF data transmission from an RF transmitter; analyzing the received RF data transmission to estimate at least one RF feature; constructing, based on the at least one RF feature, an RF fingerprint of the RF transmitter which is specific to the first RF receiving device; and modifying the RF fingerprint to generate a modified RF fingerprint for use by a second RF receiving device, by applying, to the RF fingerprint, a conversion function reflecting a differential between respective contribution values associated with each of the first and second receiving devices.

Abstract: Techniques for integrating a trusted execution platform with a function-based service framework are disclosed. For example, a method obtains an application program comprising a first set of one or more functions for execution within a secure execution area of a function-based service framework and a second set of one or more functions for execution within a non-secure execution area of the function-based service framework. A client attests an attestation delegator and the attestation delegator attests one or more secure containers prior to receipt of a function execution request to execute a function in the function-based service framework.

Abstract: Techniques for training data protection in an artificial intelligence model execution environment are disclosed. For example, a method comprises executing a first partition of an artificial intelligence model within a secure execution area of an information processing system and a second partition of the artificial intelligence model within a non-secure execution area of the information processing system, wherein data at least one of obtained and processed in the first partition of the artificial intelligence model is inaccessible to the second partition of the artificial intelligence model. Communication between the first partition and the second partition may be enabled via a model parallelism-based procedure. Data obtained in the secure execution area may comprise one or more data samples in an encrypted form usable to train the artificial intelligence model.

Abstract: Techniques for training data protection in an artificial intelligence model execution environment are disclosed. For example, a method comprises executing a first portion of an artificial intelligence model within a trusted execution area of an information processing system and a second portion of the artificial intelligence model within an untrusted execution area of the information processing system, wherein data at least one of obtained and processed in the first portion of the artificial intelligence model is inaccessible to the second portion of the artificial intelligence model. Data obtained in the trusted execution area may comprise one or more data samples in an encrypted form usable to train the artificial intelligence model.

Abstract: One example method includes extracting an expected communication specification of a service that is in development in a build pipeline, stimulating the service to exercise communication variations of the service, capturing communication traffic involving the service and one or more endpoints, comparing the captured communication traffic to the expected communication specification, and based on the comparing, detecting, in the captured communication traffic, and flagging, any anomalous communication behavior of the service.

Abstract: One example method includes receiving from a user, by a trust algorithm, primary input that comprises a user query that specifies search parameters, a list of one or more trust factors, or is automatically assigned a list of trust factors based on organizational requirements, and a respective user-specified weighting for each trust factor definition, receiving secondary system inputs and, based on the search parameters, retrieving data from the secondary system inputs, running, on the data retrieved from the secondary system inputs, one or more trust factor functions, each of which generates a respective trust factor, generating a trust score by running a trust score function on the trust factors, aggregating the data with the trust score to create a result set, and storing the result set.

Abstract: A method comprising operating at least one hardware processor for: receiving, by a radio frequency (RF) receiver, a plurality of training RF transmissions from an RF device, wherein each of said training RF transmissions is temporally associated with operational parameters and ambient parameters of said RF receiver and said RF device; at a training stage, training a machine learning classifier based, at least in part, on a training set comprising: (i) a Carrier Frequency Offset (CFO) value calculated for each of said training RF transmissions, and (ii) labels associated with said operational parameters and said ambient parameters; and at an inference stage, applying said machine learning classifier to determine whether one or more runtime RF transmissions originate from said RF device.

Abstract: A method comprising operating at least one hardware processor for: receiving, by a radio frequency (RF) receiver, a plurality of training RF transmissions from an RF device, wherein each of said training RF transmissions is temporally associated with operational parameters and ambient parameters of said RF receiver and said RF device; at a training stage, training a machine learning classifier based, at least in part, on a training set comprising: (i) a Carrier Frequency Offset (CFO) value calculated for each of said training RF transmissions, and (ii) labels associated with said operational parameters and said ambient parameters; and at an inference stage, applying said machine learning classifier to determine whether one or more runtime RF transmissions originate from said RF device.

Abstract: A method and a system for detecting an occurrence of an auto-commit operation applied to files managed by a file server compliant with write-once-read-many (WORM) rules. The method includes: allocating a plurality of non-overlapping predefined time ranges starting from a newest-changed-files time range and ending at an oldest-changed-files time range, wherein the time ranges add up to an auto-commit period associated with the auto-commit operation; repeatedly updating a count of files whose file-change-time is associated respectively with one of the allocated time ranges, wherein the updating is carried out every time the predefined time range lapses; and detecting, every time the time range lapses, an occurrence of an auto-commit operation applied to at least one of the files stored on the volume within a duration of the time range since the updating, whenever the count of the files associated with the oldest-changed-files time range is non-zero.

Abstract: A method and a system for detecting an occurrence of an auto-commit operation applied to files managed by a file server compliant with write-once-read-many (WORM) rules. The method includes: allocating a plurality of non-overlapping predefined time ranges starting from a newest-changed-files time range and ending at an oldest-changed-files time range, wherein the time ranges add up to an auto-commit period associated with the auto-commit operation; repeatedly updating a count of files whose file-change-time is associated respectively with one of the allocated time ranges, wherein the updating is carried out every time the predefined time range lapses; and detecting, every time the time range lapses, an occurrence of an auto-commit operation applied to at least one of the files stored on the volume within a duration of the time range since the updating, whenever the count of the files associated with the oldest-changed-files time range is non-zero.