Abstract: A method, a neural network, and a computer program product are provided that optimize training of neural networks using homomorphic encrypted elements and dropout algorithms for regularization. The method includes receiving, via an input to the neural network, a training dataset containing samples that are encrypted using homomorphic encryption. The method also includes determining a packing formation and selecting a dropout technique during training of the neural network based on the packing technique. The method further includes starting with a first packing formation from the training dataset, inputting the first packing formation in an iterative or recursive manner into the neural network using the selected dropout technique, with a next packing formation from the training dataset acting as an initial input that is applied to the neural network for a next iteration, until a stopping metric is produced by the neural network.

Abstract: A computer-implemented method including: training a first Generative Adversarial Network (GAN) based on original structured data; training a second GAN based on fabricated structured data that adhere to user-defined constraints; combining the first and second GANs into a combined GAN; training the combined GAN; and operating the trained combined GAN to generate new fabricated data that both imitate characteristics of the original structured data, and adhere to the user-defined constraints.

Abstract: A method, computer system, and a computer program for malware detection in containerized environments are provided. The method may include monitoring operation of a container image by receiving a plurality of system calls performed during the operation of the container image. The method further includes comparing the plurality of system calls to prior container behavior associated with previous operation of a prior container image, and identifying a deviation from the prior container behavior. The method further includes isolating a subset of the plurality of system calls and classifying the subset to a malware class of a plurality of malware classes.

Abstract: An example system includes a processor that can obtain a circuit describing operations of sequential secure computation code. The processor can modify the circuit based on a cost function. The processor can partition the circuit into a number of sub-circuits. The processor can assign the number of the sub-circuits to different processors for execution.

Abstract: Identifying and avoiding an overflow event while performing machine learning inference operations with homomorphic encryption. Prior to a first run of a machine learning inference operation, a first overflow event is created in order to determine the values that are achieved values. These values are compared to a set of user selected homomorphic encryption libraries in order to determine which parameters of the machine learning inference operation must be adjusted in order to avoid future overflow events during subsequent runs of the machine learning inference operation.

Abstract: A method, a neural network, and a computer program product are provided that optimize training of neural networks using homomorphic encrypted elements and dropout algorithms for regularization. The method includes receiving, via an input to the neural network, a training dataset containing samples that are encrypted using homomorphic encryption. The method also includes determining a packing formation and selecting a dropout technique during training of the neural network based on the packing technique. The method further includes starting with a first packing formation from the training dataset, inputting the first packing formation in an iterative or recursive manner into the neural network using the selected dropout technique, with a next packing formation from the training dataset acting as an initial input that is applied to the neural network for a next iteration, until a stopping metric is produced by the neural network.

Abstract: A method for automatically migrating infrastructure as code (IaC) from a first cloud infrastructure platform to a second cloud infrastructure platform is provided. The method may include receiving an original IaC comprising a first type of coding language. The method may further include using natural language processing to map a connection between the first type of coding language and a second type of coding language. The method may further include based on the mapped connection, using the NLP to automatically generate a partial translation of the first type of coding language to the second type of coding language. The method may further include using a machine learning algorithm to correct at least one inaccuracy in the partial translation. The method may further include generating a complete translation and implementing a second IaC on the second cloud infrastructure platform based on the complete translation.

Abstract: Embodiments of the present systems and methods may provide techniques to distinguish between data categories. For example, a method implemented in a computer system may comprise obtaining, at the computer system, a plurality of data strings in different categories, each category having a same string pattern, determining a loose string format and a set of restrictions based on at least one string pattern, classifying the plurality of data strings to respective different categories based on a loose string format of the data strings and on the restrictions on the data strings of the different categories using a classification score indicating utilizing restriction information of other categories when determining the matching of a category, and decreasing the classification score if a mean restriction matching proportion is not part of a category or is a threshold amount above an expected mean restriction matching proportion.

Abstract: An example system includes a processor that can obtain a circuit describing operations of sequential secure computation code. The processor can modify the circuit based on a cost function. The processor can partition the circuit into a number of sub-circuits. The processor can assign the number of the sub-circuits to different processors for execution.

Abstract: A method for automatically migrating infrastructure as code (IaC) from a first cloud infrastructure platform to a second cloud infrastructure platform is provided. The method may include receiving an original IaC comprising a first type of coding language. The method may further include using natural language processing to map a connection between the first type of coding language and a second type of coding language. The method may further include based on the mapped connection, using the NLP to automatically generate a partial translation of the first type of coding language to the second type of coding language. The method may further include using a machine learning algorithm to correct at least one inaccuracy in the partial translation. The method may further include generating a complete translation and implementing a second IaC on the second cloud infrastructure platform based on the complete translation.

Abstract: The present invention relates to novel techniques for monitoring changes to source code of Infrastructure as Code systems to detect attempted anomalous changes and block such changes from the code. For example, a method may comprise learning a security architecture and history of an infrastructure as code system to be deployed in at least one cloud account, monitoring changes to source code of the infrastructure as code system that are made before deployment of the infrastructure as code system to detect an anomaly, determining whether the detected anomaly affects regulated resources of the infrastructure as code system, and blocking changes to the source code of the infrastructure as code system that produce the detected anomaly that affects regulated resources of the infrastructure as code system.

Abstract: An efficient packing method that will optimize use of the homomorphic encryption ciphertext slots, trading-off size, latency, and throughput. Technology for working with tensors (multi-dimensional arrays) in a system that imposes tiles, that is, fixed-size vectors. An example of a system that imposes tiles are homomorphic encryption schemes, where each ciphertext encrypts a vector of some fixed size. The tensors are packed into tiles and then manipulated via operations on those tiles. Also, syntax for notation for describing packing details. This technology interprets the tiles as multi-dimensional arrays, and combines them to cover enough space to hold the tensor. An efficient summation algorithm can then sum over any dimension of this tile tensor construct that exists in the physical or logical addressing space of a computer data memory.

Abstract: An efficient packing method that will optimize use of the homomorphic encryption ciphertext slots, trading-off size, latency, and throughput. Technology for working with tensors (multi-dimensional arrays) in a system that imposes tiles, that is, fixed-size vectors. An example of a system that imposes tiles are homomorphic encryption schemes, where each ciphertext encrypts a vector of some fixed size. The tensors are packed into tiles and then manipulated via operations on those tiles. Also, syntax for notation for describing packing details. This technology interprets the tiles as multi-dimensional arrays, and combines them to cover enough space to hold the tensor. An efficient summation algorithm can then sum over any dimension of this tile tensor construct that exists in the physical or logical addressing space of a computer data memory.

Abstract: The present invention relates to novel techniques for monitoring changes to source code of Infrastructure as Code systems to detect attempted anomalous changes and block such changes from the code. For example, a method may comprise learning a security architecture and history of an infrastructure as code system to be deployed in at least one cloud account, monitoring changes to source code of the infrastructure as code system that are made before deployment of the infrastructure as code system to detect an anomaly, determining whether the detected anomaly affects regulated resources of the infrastructure as code system, and blocking changes to the source code of the infrastructure as code system that produce the detected anomaly that affects regulated resources of the infrastructure as code system.

Abstract: Embodiments may provide distance computations on homomorphic and/or functional encrypted vectors while detecting whether the resulting distance has wrapped around due to the vectors having elements not in an allowed range. A method of user authentication processing may comprise receiving and storing enrollment information from a client computer system, the enrollment information comprising a template of authentication data and at least one additional encrypted vector, receiving an additional template to be used to authenticate the user from the client computer system, authenticating the user using the received additional template using the stored template and the stored at least one additional encrypted vector, and determining that authentication is successful when the received additional template matches the stored template and is valid based on the stored at least one additional encrypted vector.

Abstract: Embodiments may provide techniques to detect cyber-security events in IoT data traffic that provide improved detection accuracy and preservation of privacy.

Abstract: Embodiments may provide distance computations on homomorphic and/or functional encrypted vectors while detecting whether the resulting distance has wrapped around due to the vectors having elements not in an allowed range. A method of user authentication processing may comprise receiving and storing enrollment information from a client computer system, the enrollment information comprising a template of authentication data and at least one additional encrypted vector, receiving an additional template to be used to authenticate the user from the client computer system, authenticating the user using the received additional template using the stored template and the stored at least one additional encrypted vector, and determining that authentication is successful when the received additional template matches the stored template and is valid based on the stored at least one additional encrypted vector.

Abstract: A data manager determines an appropriate number of clusters for continuous data using unsupervised learning. The data manager selects an appropriate number of clusters based on at least one temporal stability measure between continuous data from at least two time intervals.

Abstract: Embodiments may provide techniques to detect cyber-security events in IoT data traffic that provide improved detection accuracy and preservation of privacy.

Abstract: Embodiments of the present systems and methods may provide techniques to distinguish between data categories. For example, a method implemented in a computer system may comprise obtaining, at the computer system, a plurality of data strings in different categories, each category having a same string pattern, determining a loose string format and a set of restrictions based on at least one string pattern, classifying the plurality of data strings to respective different categories based on a loose string format of the data strings and on the restrictions on the data strings of the different categories using a classification score indicating utilizing restriction information of other categories when determining the matching of a category, and decreasing the classification score if a mean restriction matching proportion is not part of a category or is a threshold amount above an expected mean restriction matching proportion.