Abstract: A machine learning (ML) model architect identifies a problem space for a ML model. The ML model architect then selects a cell architecture skeleton. The ML model architecture defines a set of operations for the cell architecture skeleton. An overparameterized model may be built based at least in part on the cell architecture skeleton and the set of operations for the problem space. The overparameterized model may be reduced to generate a reduced model. The reduced model may be trained using a training dataset to produce a trained, reduced model. Suboptimal operations may be pruned from the trained reduced model to produce a pruned reduced model. Reverse reduction processing may then be performed on the pruned reduced model to generate an optimal cell architecture model for the identified problem space.

Abstract: Systems, methods, devices, and apparatus are discussed for detecting potential insider attacks associated with a communication network and associated devices based on images that encode behavior activities of an insider performed in relation to a communication network, in conjunction with non-dynamic data related to the insider.

Abstract: Systems, methods, devices, and apparatus are discussed for generating data that appears to be an insider attack.

Abstract: A system is disclosed. The system includes at least one physical memory device to store report generation logic and one or more processors coupled with the at least one physical memory device to execute the report generation logic to receive image data including a behavioral information, receive text data comprising a plurality of candidate reports, generate a plurality of image-report encodings based on the image data and the text data and generate a report based on the image-report encodings.

Abstract: Systems, methods, devices, and apparatus are discussed for generating data that appears to be an insider attack.

Abstract: Systems, devices, and methods are disclosed in relation to a system for natural language based message categorization designed to identify text from a particular topic from a potentially inexhaustible set of potential topics. In one of many possible implementations, a vector space model is first used to translate text into a vector representation. This vector is used to determine if the text can be recreated by swapping words and phrases from a training corpus of documents. This is done by determining if the vector is within the conical span of the vector representations of the text in the training corpus of documents. Span composition is evaluated by a two vector boolean comparison, enabling great computational complexity and short-circuiting enabling fast real-time topic determination.

Abstract: Systems, devices, and methods are disclosed that may be used for identifying potential insider attacks on a computer network.

Abstract: Systems, devices, and methods are disclosed in relation to a vector space model that may be used to characterize a category of messages. In one of many possible implementations, the frequency of words found within a piece of text is determined. These frequencies are compared against the frequencies of words within a given corpus like the Oxford English Corpus by first converting the frequencies to probabilities via the inverse cumulative distribution function assuming a normal distribution of frequencies then via taking the absolute difference in frequencies. A small difference reduces the weight of the given word whereas a large weight increases the weight of the word, leading to excellent word ranking for automated feature selection filtering without the need for a negative corpus.

Abstract: Systems, devices, and methods are disclosed for encoding behavioral information into an image format to facilitate image based behavioral identification.

Abstract: Systems, devices, and methods are disclosed that may be used for identifying potential insider attacks on a computer network.

Abstract: Systems, devices, and methods are disclosed in relation to a vector space model that may be used to characterize a category of messages. In one of many possible implementations, the frequency of words found within a piece of text is determined. These frequencies are compared against the frequencies of words within a given corpus like the Oxford English Corpus by first converting the frequencies to probabilities via the inverse cumulative distribution function assuming a normal distribution of frequencies then via taking the absolute difference in frequencies. A small difference reduces the weight of the given word whereas a large weight increases the weight of the word, leading to excellent word ranking for automated feature selection filtering without the need for a negative corpus.

Abstract: Systems, devices, and methods are disclosed in relation to a system for natural language based message categorization designed to identify text from a particular topic from a potentially inexhaustible set of potential topics. In one of many possible implementations, a vector space model is first used to translate text into a vector representation. This vector is used to determine if the text can be recreated by swapping words and phrases from a training corpus of documents. This is done by determining if the vector is within the conical span of the vector representations of the text in the training corpus of documents. Span composition is evaluated by a two vector boolean comparison, enabling great computational complexity and short-circuiting enabling fast real-time topic determination.

Abstract: Systems, devices, and methods are disclosed in relation to a system for natural language based message categorization designed to identify text from a particular topic from a potentially inexhaustible set of potential topics. In one of many possible implementations, a vector space model is first used to translate text into a vector representation. This vector is used to determine if the text can be recreated by swapping words and phrases from a training corpus of documents. This is done by determining if the vector is within the conical span of the vector representations of the text in the training corpus of documents. Span composition is evaluated by a two vector boolean comparison, enabling great computational complexity and short-circuiting enabling fast real-time topic determination.

Abstract: Systems, devices, and methods are disclosed in relation to a system for natural language based message categorization designed to identify text from a particular topic from a potentially inexhaustible set of potential topics. In one of many possible implementations, a vector space model is first used to translate text into a vector representation. This vector is used to determine if the text can be recreated by swapping words and phrases from a training corpus of documents. This is done by determining if the vector is within the conical span of the vector representations of the text in the training corpus of documents. Span composition is evaluated by a two vector boolean comparison, enabling great computational complexity and short-circuiting enabling fast real-time topic determination.

Abstract: Systems, methods, devices, and apparatus are discussed for detecting relatively rare attacks in a communication network, and in some cases for detecting insider attacks on a communication network.

Abstract: Systems, methods, devices, and apparatus are discussed for generating data that appears to be an insider attack.

Abstract: Systems, devices, and methods are disclosed for encoding behavioral information into an image format to facilitate image based behavioral identification.

Abstract: Systems, devices, and methods are disclosed for encoding behavioral information into an image format to facilitate image based behavioral identification.

Abstract: Systems, devices, and methods are disclosed that may be used for identifying potential insider attacks on a computer network.

Abstract: Systems, devices, and methods are disclosed in relation to a vector space model that may be used to characterize a category of messages. In one of many possible implementations, the frequency of words found within a piece of text is determined. These frequencies are compared against the frequencies of words within a given corpus like the Oxford English Corpus by first converting the frequencies to probabilities via the inverse cumulative distribution function assuming a normal distribution of frequencies then via taking the absolute difference in frequencies. A small difference reduces the weight of the given word whereas a large weight increases the weight of the word, leading to excellent word ranking for automated feature selection filtering without the need for a negative corpus.