A device includes a plurality of blocks. Each block of the plurality of blocks includes a plurality of rows. Each row of the plurality of rows includes a plurality of configurable elements and a routing line, whereby each configurable element of the plurality of configurable elements includes a data analysis element comprising a plurality of memory cells, wherein the data analysis element is configured to analyze at least a portion of a data stream and to output a result of the analysis. Each configurable element of the plurality of configurable elements also includes a multiplexer configured to transmit the result to the routing line.

A protocol state fuzzing method for security of a control plane of a distributed software-defined network is provided. The protocol state fuzzing method includes receiving input alphabets being abstract symbols of a protocol message in an ambusher of a distributed network operating system (NOS), converting the input alphabets into the protocol message, and sending the protocol message to a cluster, monitoring, by the cluster, intercommunication between instances in the distributed NOS, and selecting a set of sequences executable in the cluster and searching a cluster log for an output by executing the sequence to generate an attack result.

The example embodiments are directed to a continuously expanding cross-domain featuring engineering system. In one example, a method may include one or more of storing predictive features in a cross-domain data store, the predictive features previously used in machine learning modeling in a plurality of different domains, receiving data of an asset included in a target domain and information about an evaluation attribute associated with the asset in the target domain, determining a predictive feature in the received data based on a previously used predictive feature stored in the cross-domain data store which is associated with a machine learning model in a different domain and the evaluation attribute, and outputting the determined predictive feature for display via a user interface.

The example embodiments are directed to a continuously expanding cross-domain featuring engineering system. In one example, a method may include one or more of storing predictive features in a cross-domain data store, the predictive features previously used in machine learning modeling in a plurality of different domains, receiving data of an asset included in a target domain and information about an evaluation attribute associated with the asset in the target domain, determining a predictive feature in the received data based on a previously used predictive feature stored in the cross-domain data store which is associated with a machine learning model in a different domain and the evaluation attribute, and outputting the determined predictive feature for display via a user interface.

A semiconductor device includes three integrated circuits. One of the integrated circuits includes: a first connector configured to connect to a device; and a transmitter. The transmitter is configured to transmit to another integrated circuit, first data on each of a plurality of pieces of packet data. The transmitter is also configured to, when the first connector is connected to the device, while a second controller is performing a second process, transmit, to a first controller, a request to process data transmitted from the device.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automated management of campaigns using scripted rules.

Some examples relate generally to managing and storing data, and more specifically to the real-time detection of ransomware, system (or insider) threats, or the misappropriation of credentials by using file system audit events.

Computing systems that provide a modularized infrastructure for training Generative Adversarial Networks (GANs) are provided herein. For example, the modularized infrastructure can include a lightweight library designed to make it easy to train and evaluate GANs. A user can interact with and/or build upon the modularized infrastructure to easily train GANs. The modularized infrastructure can include a number of distinct sets of code that handle various stages of and operations within the GAN training process. The sets of code can be modular. That is, the sets of code can be designed to exist independently yet be easily and intuitively combinable. Thus, the user can employ some or all of the sets of code or can replace a certain set of code with a set of custom-code while still generating a workable combination.

Computing systems that provide a modularized infrastructure for training Generative Adversarial Networks (GANs) are provided herein. For example, the modularized infrastructure can include a lightweight library designed to make it easy to train and evaluate GANs. A user can interact with and/or build upon the modularized infrastructure to easily train GANs. The modularized infrastructure can include a number of distinct sets of code that handle various stages of and operations within the GAN training process. The sets of code can be modular. That is, the sets of code can be designed to exist independently yet be easily and intuitively combinable. Thus, the user can employ some or all of the sets of code or can replace a certain set of code with a set of custom-code while still generating a workable combination.

A system and method for processing data by accessing data sets for a plurality of variables in at least one data store; associating a plurality of the data sets as at least one variable type; storing in a data store a plurality of operation definitions defining a plurality of operations on at least one of said at least one variable type; receiving from a user interface a selection of at least one operation definition and at least one data set of said at least one variable type operated on by the selected at least one operation definition; and processing the at least one data set in response to the selection according to the at least one operation definition to generate a derived data set.