Abstract: A set and a second set of collections of forecasted feature vectors are selected from a repository for a future time window, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set and a collection in the second set indicating an event related to the cyber-attack in a first region and a second event in a second region, respectively, of the environment at a discrete time. The set of collections is input at a first input and the second set of collections is input at a second input in the LSTM. The events corresponding to the collections are classified into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase that corresponds to the class is predicted as likely to occur during the future time window in the region.

Abstract: A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over a feature vectors of the second collection with a corresponding feature vector of a fourth collection. The second and the fourth collections have a property similar to one another. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A transformed image is received. The transformed image includes an other-than-visible light image that has been captured using a transformation device. A region of the transformed image is isolated, the region being less than an entirety of the transformed image. By applying to the region a convolutional Neural Network (CNN) which executes using a processor and a memory, and by processing only the region of the transformed image, an object of interest is detected in the region. Upon detecting, an indication is produced to indicate the presence of the object of interest in the region.

Abstract: A first collection including a pattern of life (POL) feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by combining a vector of the second collection with a corresponding vector of a different collection. Using a forecasting configuration, a POL feature vector of the third collection is aged to generate a changed POL feature vector containing POL feature values expected at a future time. The changed POL feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including an analytical feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection. Using a forecasting configuration, an analytical feature vector of the third collection is aged to generate a changed analytical feature vector containing analytical feature values expected at a future time. The changed analytical feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over a feature vectors of the second collection with a corresponding feature vector of a fourth collection. The second and the fourth collections have a property similar to one another. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a pattern of life (POL) feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by combining a vector of the second collection with a corresponding vector of a different collection. Using a forecasting configuration, a POL feature vector of the third collection is aged to generate a changed POL feature vector containing POL feature values expected at a future time. The changed POL feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection. The second and the fourth collections have a property similar to one another. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a pattern of life (POL) feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection. Using a forecasting configuration, a POL feature vector of the third collection is aged to generate a changed POL feature vector containing POL feature values expected at a future time. The changed POL feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one the vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection, or both. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A set of collections of forecasted feature vectors is selected from a repository for a future time window after a present time, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set having feature vectors that are indicative of an event related to the cyber-attack in a region of the environment at a discrete time. The events corresponding to the collections in the set are classified into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase is determined that corresponds to the class. The determined phase is predicted as likely to occur during the future time window in the region.

Abstract: A transformed image is received. The transformed image includes an other-than-visible light image that has been captured using a transformation device. A region of the transformed image is isolated, the region being less than an entirety of the transformed image. By applying to the region a convolutional Neural Network (CNN) which executes using a processor and a memory, and by processing only the region of the transformed image, an object of interest is detected in the region. Upon detecting, an indication is produced to indicate the presence of the object of interest in the region.

Abstract: In an approach to face recognition in an image, one or more computer processors receive an image that includes at least one face and one or more face parts. The one or more computer processors detect the one or more face parts in the image with a face component model. The one or more computer processors cluster the detected one or more face parts with one or more stored images. The one or more computer processors extract, from the clustered images, one or more face descriptors. The one or more computer processors determine a recognition score of the at least one face, based, at least in part, on the extracted one or more face descriptors.

Abstract: A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by migrating, at least one of a vectors of the second collection with a corresponding vector of a fourth collection. The second and the fourth collections have a property distinct from one another. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including an analytical feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection. Using a forecasting configuration, an analytical feature vector of the third collection is aged to generate a changed analytical feature vector containing analytical feature values expected at a future time. The changed analytical feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A set of collections of forecasted feature vectors is selected from a repository for a future time window after a present time, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set having feature vectors that are indicative of an event related to the cyber-attack in a region of the environment at a discrete time. The events corresponding to the collections in the set are classified into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase is determined that corresponds to the class. The determined phase is predicted as likely to occur during the future time window in the region.

Abstract: A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one the vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection, or both. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A first collection including a pattern of life (POL) feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection. Using a forecasting configuration, a POL feature vector of the third collection is aged to generate a changed POL feature vector containing POL feature values expected at a future time. The changed POL feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Abstract: A set and a second set of collections of forecasted feature vectors are selected from a repository for a future time window, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set and a collection in the second set indicating an event related to the cyber-attack in a first region and a second event in a second region, respectively, of the environment at a discrete time. The set of collections is input at a first input and the second set of collections is input at a second input in the LSTM. The events corresponding to the collections are classified into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase that corresponds to the class is predicted as likely to occur during the future time window in the region.

Abstract: A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection. The second and the fourth collections have a property similar to one another. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.