Abstract: A method for tracking an object can include receiving first data input including first feature values of features that indicate a first position. The method can further include generating a first grid of cells representing an object track with the received feature values within an extent of the first grid of cells. The method can further include receiving second data input including second feature values of the features that indicate a second position. The method can further include, in response to determining the second feature values are within the extent of the first grid of cells adding a point corresponding to the second feature values to the first grid of cells to associate the point to an object track.

Abstract: A method can include identifying a first key value of a first cell of a first grid of grids of cells to which a first feature maps, embedding the first grid into each cell of a second grid, identifying a second key value of a second cell of the second grid to which a second feature maps, the second key value representative of the first and second key values, comparing the identified key value to the key values of a memory, in response to determining the identified key value is in the memory, and providing data indicating a class associated with the identified key value in the memory.

Abstract: Gene expression programming-based behavior monitoring is disclosed. A machine receives, as input, a plurality of data examples. A method can include receiving data indicating behaviors of the device, determining, using a gene expression programming (GEP) method, a data model that explains the data, and comparing further data indicating further behavior of the device to the data model to determine whether the further behavior is explained by the data model.

Abstract: A method can include determining a cell of a grid to which a first feature and a second feature of each of a plurality of input/output examples maps, determining an average of respective features of the cell to generate respective level 2 synthetic feature vectors, for each cell with an input/output example of the input/output examples mapped thereto, generating a sub-grid of cells and map the input/output examples mapped to a cell of the sub-grid, determining an average of respective features to generate respective level 1 synthetic feature vectors comprising the average of the respective features, training the ML technique using the level 2 synthetic feature vector, testing the trained ML technique using the level 1 synthetic feature vector of each sub-cell, and further testing the trained ML technique using the input/output examples to generate a class and confidence for each of the input/output examples.

Abstract: A method of generating cyber chaff can include determining a cell of a grid of cells to which a first feature and a second feature of user data maps, identifying a cell type of the cell, the cell type indicating whether the cell is an active cell, an inactive cell, or a sub-process cell, and providing cyber chaff based on cyber chaff data associated with either (a) one or more cells of the inactive cell type or (b) one or more cells of the sub-process cell type.

Abstract: A method of identifying anomalous behavior can include determining a first feature value and a second feature value of a series of numbers, identifying whether the determined first and second feature values map to a cell that is a part of a group based on spatial voting (SV) grid data, the SV grid data indicating an extent of each cell in a grid of cells, a number of rows and columns of cells in a group of cells, and, for each group of cells, a point in the center cell of the group of cells and a unique group number, and in response to identifying that the determined first and second feature values do not map to a cell of the grid of cells that is a part of a group, tagging the first and second feature values as corresponding to an anomalous behavior.

Abstract: Embedded convolutional data modeling is disclosed. A machine receives, as input, a plurality of data examples. The machine generates an embedded convolutional data model for the data examples. The machine outputs a source code for the model.

Abstract: A method of identifying anomalous behavior can include transforming input data to a series of numbers, determining first features and second features of the series of numbers that, given the same numbers in a different order, produce a different value for the respective feature, encoding the series of numbers by a key value indicating a number associated with a cell of a grid of cells to which the first and second features map, and determining, based on whether the key value has been previously provided, whether the input is anomalous.

Abstract: A method of encryption using spatial voting can include determining a first feature and a second feature of user data, wherein the first feature and the second feature correspond to a cell of a subset of cells of a grid of cells, each cell of the subset of cells including a character associated therewith, altering one or more values of the first feature and the second feature to generate an altered first feature and an altered second feature, and concatenating the altered first feature and the altered second feature to generate encrypted data.

Abstract: A method can include identifying a first key value of a first cell of a first grid of grids of cells to which a first feature maps, embedding the first grid into each cell of a second grid, identifying a second key value of a second cell of the second grid to which a second feature maps, the second key value representative of the first and second key values, comparing the identified key value to the key values of a memory, in response to determining the identified key value is in the memory, and providing data indicating a class associated with the identified key value in the memory.

Abstract: A method can include identifying, by processing circuitry of a device, a row of rows and/or column of columns to which a first feature and a second feature of an input maps, comparing the identified row and/or column to a row run length encoding (RLE) in a memory of the device that indicates, for each row, whether one or more cells in the row include an input mapped thereto or a column RLE in a memory of the device that indicates, for each column, whether one or more cells in the column include an input mapped thereto, respectively, and determining the input data is anomalous in response to determining either the row RLE indicates that no inputs are mapped to the row to which the input maps, or the column RLE indicates that no inputs are mapped to the column to which the input maps.

Abstract: Gene expression programming-based behavior monitoring is disclosed. A machine receives, as input, a plurality of data examples. A method can include receiving data indicating behaviors of the device, determining, using a gene expression programming (GEP) method, a data model that explains the data, and comparing further data indicating further behavior of the device to the data model to determine whether the further behavior is explained by the data model.

Abstract: Embedded convolutional data modeling is disclosed. A machine receives, as input, a plurality of data examples. The machine generates an embedded convolutional data model for the data examples. The machine outputs a source code for the model.

Abstract: A method of identifying anomalous behavior can include determining a first feature value and a second feature value of a series of numbers, identifying whether the determined first and second feature values map to a cell that is a part of a group based on spatial voting (SV) grid data, the SV grid data indicating an extent of each cell in a grid of cells, a number of rows and columns of cells in a group of cells, and, for each group of cells, a point in the center cell of the group of cells and a unique group number, and in response to identifying that the determined first and second feature values do not map to a cell of the grid of cells that is a part of a group, tagging the first and second feature values as corresponding to an anomalous behavior.

Abstract: A method can include identifying, by processing circuitry of a device, a row of rows and/or column of columns to which a first feature and a second feature of an input maps, comparing the identified row and/or column to a row run length encoding (RLE) in a memory of the device that indicates, for each row, whether one or more cells in the row include an input mapped thereto or a column RLE in a memory of the device that indicates, for each column, whether one or more cells in the column include an input mapped thereto, respectively, and determining the input data is anomalous in response to determining either the row RLE indicates that no inputs are mapped to the row to which the input maps, or the column RLE indicates that no inputs are mapped to the column to which the input maps.

Abstract: A method of encryption using spatial voting can include determining a first feature and a second feature of user data, wherein the first feature and the second feature correspond to a cell of a subset of cells of a grid of cells, each cell of the subset of cells including a character associated therewith, altering one or more values of the first feature and the second feature to generate an altered first feature and an altered second feature, and concatenating the altered first feature and the altered second feature to generate encrypted data.

Abstract: Presented herein are alternatives to design of experiments. A method can include sampling a model that explains a measurement corpus of measurement data to generate a sampled model, identifying an invalid region of the sampled model, determining whether a device will operate within the identified invalid region, if the device will operate within the identified invalid region, causing further measurement data to be captured in the identified invalid region, and generating a new model, based only on the further measurement data, to explain device operation within the identified invalid region that augments the sampled model to explain the device behavior.

Abstract: A method of identifying anomalous behavior can include transforming input data to a series of numbers, determining first features and second features of the series of numbers that, given the same numbers in a different order, produce a different value for the respective feature, encoding the series of numbers by a key value indicating a number associated with a cell of a grid of cells to which the first and second features map, and determining, based on whether the key value has been previously provided, whether the input is anomalous.

Abstract: A method of generating cyber chaff can include determining a cell of a grid of cells to which a first feature and a second feature of user data maps, identifying a cell type of the cell, the cell type indicating whether the cell is an active cell, an inactive cell, or a sub-process cell, and providing cyber chaff based on cyber chaff data associated with either (a) one or more cells of the inactive cell type or (b) one or more cells of the sub-process cell type.

Abstract: A method can include determining a cell of a grid to which a first feature and a second feature of each of a plurality of input/output examples maps, determining an average of respective features of the cell to generate respective level 2 synthetic feature vectors, for each cell with an input/output example of the input/output examples mapped thereto, generating a sub-grid of cells and map the input/output examples mapped to a cell of the sub-grid, determining an average of respective features to generate respective level 1 synthetic feature vectors comprising the average of the respective features, training the ML technique using the level 2 synthetic feature vector, testing the trained ML technique using the level 1 synthetic feature vector of each sub-cell, and further testing the trained ML technique using the input/output examples to generate a class and confidence for each of the input/output examples.