Abstract: A method for synthetically generating a point cloud of radar or LIDAR reflections, a reflection indicating at least one location at which radar or LIDAR interrogating radiation has been reflected. In the method, distribution functions which according to a random distribution provide samples in each case for at least one of the variables contained in the radar or LIDAR reflections are provided; synthetic reflections are generated by drawing samples in each case from the distribution functions for variables contained in the radar or LIDAR reflections, one of multiple distribution functions being selected according to at least one selection random distribution in order to draw each sample; the synthetic reflections are combined to form the sought point cloud.

Abstract: A method is disclosed for converting source radar data of a source configuration of a radar system target radar data of a target configuration. The method comprises: providing a source array of grid cells for source reflex locations; determining, for each respective grid cell in the source array, a probability or frequency that source reflex locations are located in the respective grid cell; forming a source tensor including the source array populated with the probability or frequency for each grid cell; transforming the source tensor into a target tensor including a target array of grid cells for the target reflex locations and indicating the probabilities or frequencies of the target reflex locations for each respective grid cell; and generating the target radar data by sampling the location coordinates of the target reflex locations.

Abstract: A device and a computer-implemented method for classifying data, in particular for a Controller Area Network or an automotive Ethernet network. A plurality of messages is received from a communications network. A message that has a predefined message type is selected for an input variable for an input model of a plurality of input models of an artificial neural network associated with the predefined message type. The input variable is determined as a function of the message, and in an output area of the artificial neural network a prediction is output that is usable for classifying the message as a function of the input variable, or a reconstruction of an input variable is output that is usable for classifying the message as a function of this input variable.

Abstract: A method for monitored training of a neural network. In the method, training examples including training measured data and associated training output variables are provided; a spatial region, which contains at least a part of the locations indicated by the training measured data of a training example, is subdivided into a grid made up of adjoining cells; for each cell, values of the measured variables contained in the training measured data for all locations in this cell are aggregated to form values of the measured variables which relate to this cell; these aggregated values of the measured variables are mapped by the neural network on one or multiple output variables; deviations of these output variables from the training output variables are assessed using a predefined cost function; parameters of the neural network are optimized.

Abstract: A method for aggregating a dataset, which respectively assigns an output variable value to a plurality of input variable vectors, into a function term. In the method, one or more elementary function expression(s) from an alphabet is/are sampled using a neural transform network. The elementary function expressions are assembled to form one or more candidate function term(s). When the candidate function term(s) is/are complete, the input variables are mapped to associated candidate output variable values using each candidate function term. A deviation between candidate output variable values and corresponding output variable values of the dataset is evaluated using a predefined metric. It is checked whether a predefined abort condition is satisfied. If the abort condition has not been satisfied, parameters which characterize the behavior of the transformer network are updated and branching back for sampling elementary function expressions using the transformer network takes place.

Abstract: A method for synthetically generating a point cloud of radar or LIDAR reflections, a reflection indicating at least one location at which radar or LIDAR interrogating radiation has been reflected. In the method, distribution functions which according to a random distribution provide samples in each case for at least one of the variables contained in the radar or LIDAR reflections are provided; synthetic reflections are generated by drawing samples in each case from the distribution functions for variables contained in the radar or LIDAR reflections, one of multiple distribution functions being selected according to at least one selection random distribution in order to draw each sample; the synthetic reflections are combined to form the sought point cloud.

Abstract: A computer-implemented method for detecting a manipulation of a technical device. The method includes: providing time characteristics of operating variables having system variable(s) and/or a correction variable for an intervention in the technical device which correspond to time series of values of the operating variables for each of consecutive time steps; using a data-based manipulation detection model in each current time step to ascertain one or more output variable(s) that correspond at least to a portion of the operating variables as a function of input variables which include at least a portion of the operating variables. The manipulation detection model includes an autoencoder having a first recurrent neural network, a prediction model having a second recurrent neural network, and an evaluation model, the outputs of the autoencoder and the prediction model being combined with one another and then conveyed to an evaluation model for an ascertainment of the output variables.

Abstract: A method for ascertaining a transformation, which converts source measured data recorded using a source configuration of a measuring system at a scenery, into target measured data, which a target configuration of the measuring system would record at the same scenery. In the method: training source measured data recorded using the source configuration at training sceneries are provided; an approach is predefined, according to which the target measured data result from the source measured data by application of predefined filter operation(s) to the source measured data; the training source measured data are mapped by application of the filter operation on target measured data; the trainable model is trained with the goal of bringing the resulting filter operation, and/or the target measured data generated thereby into harmony with a predefined piece of additional information and/or condition; the approach completed by the trained model is provided as the sought-after transformation.

Abstract: A method for detecting manipulation of a technical device, particularly a technical device in a motor vehicle, especially an exhaust-gas treatment device. The method includes: providing a time series of an input vector having one or more system variables and having at least one manipulated variable for an intervention in the technical device; utilizing a data-based manipulation detection model which includes a recurrent neural network that is designed to determine a state vector as a function of the input vector, and an autoencoder which is designed to determine a reconstructed vector as a function of the state vector, detecting an anomaly as a function of a reconstruction error, which is a function of the reconstructed vector; and detecting a manipulation as a function of the reconstruction error.

Abstract: A method is disclosed for converting source radar data of a source configuration of a radar system target radar data of a target configuration. The method comprises: providing a source array of grid cells for source reflex locations; determining, for each respective grid cell in the source array, a probability or frequency that source reflex locations are located in the respective grid cell; forming a source tensor including the source array populated with the probability or frequency for each grid cell; transforming the source tensor into a target tensor including a target array of grid cells for the target reflex locations and indicating the probabilities or frequencies of the target reflex locations for each respective grid cell; and generating the target radar data by sampling the location coordinates of the target reflex locations.

Abstract: An apparatus and a method for calibrating a system for recognizing attempts to penetrate into a computer network, in particular of a motor vehicle, at least one parameter being estimated on the basis of a data set, the data set encompassing values that characterize a detected occurrence of messages in the computer network; a distribution function being determined on the basis of the at least one parameter; an inverse of the distribution function being determined; and at least one limit for the values being calibrated, on the basis of the inverse, in a rule for rule-based recognition of attempts to penetrate into the computer network.

Abstract: A method for ascertaining an anomaly in a communications network. In a first phase, a discriminator is trained to recognize whether messages transmitted over the communications network are indicative of the anomaly existing; during training, normal data and artificial data produced by a generator are fed to the discriminator, and, in response, the discriminator is trained to recognize that normal data being fed thereto connotes no anomaly, and artificial data being fed thereto connotes an anomaly. In a second phase, the generator is trained to produce artificial data which, when fed to the discriminator, are classified with the greatest possible probability as normal data. In a third phase, contents of messages received over the communications network are fed as an input variable to the discriminator; an output variable is ascertained, and the decision as to whether the anomaly exists or not being made as a function of the output variable.

Abstract: A method for creating rules for recognizing anomalies in a data stream of data packets. The method includes: providing a reference time signal having successive reference points in time; for at least two data portions from one or multiple data packets determined by a selected data packet type in a data stream section, ascertaining a time series of successive values of the relevant data portion, the values of the time series corresponding to the values of the relevant data portion or being a function of these values, the values of the relevant data portion each being assigned to a respective reference point in time of the respective reference points in time; carrying out a correlation method in order to ascertain, in each case, one correlation value for at least two different time series; creating a rule for the rule-based anomaly recognition method as a function of the ascertained correlation values.

Abstract: A device and a computer-implemented method for classifying data, in particular for a Controller Area Network or an automotive Ethernet network. A plurality of messages is received from a communications network. A message that has a predefined message type is selected for an input variable for an input model of a plurality of input models of an artificial neural network associated with the predefined message type. The input variable is determined as a function of the message, and in an output area of the artificial neural network a prediction is output that is usable for classifying the message as a function of the input variable, or a reconstruction of an input variable is output that is usable for classifying the message as a function of this input variable.

Abstract: A method for ascertaining an anomaly in a communications network. In a first phase, a discriminator is trained to recognize whether messages transmitted over the communications network are indicative of the anomaly existing; during training, normal data and artificial data produced by a generator are fed to the discriminator, and, in response, the discriminator is trained to recognize that normal data being fed thereto connotes no anomaly, and artificial data being fed thereto connotes an anomaly. In a second phase, the generator is trained to produce artificial data which, when fed to the discriminator, are classified with the greatest possible probability as normal data. In a third phase, contents of messages received over the communications network are fed as an input variable to the discriminator; an output variable is ascertained, and the decision as to whether the anomaly exists or not being made as a function of the output variable.

Abstract: An apparatus and a method for calibrating a system for recognizing attempts to penetrate into a computer network, in particular of a motor vehicle, at least one parameter being estimated on the basis of a data set, the data set encompassing values that characterize a detected occurrence of messages in the computer network; a distribution function being determined on the basis of the at least one parameter; an inverse of the distribution function being determined; and at least one limit for the values being calibrated, on the basis of the inverse, in a rule for rule-based recognition of attempts to penetrate into the computer network.

Abstract: A method for the automatic recognition of anomalies in a data stream in a communication network. The method includes providing a trained variational autoencoder that is trained on non-faulty data packets, with specification of a reference distribution of latent quantities, indicated by reference distribution parameters; determining one or more distribution parameters as a function of an input quantity vector applied to the trained variational autoencoder, which vector is determined by one or more data packets; and recognizing the one or more data packets as anomalous data packet(s) as a function of the one or more distribution parameters.

Abstract: A method for creating rules for recognizing anomalies in a data stream of data packets. The method includes: providing a reference time signal having successive reference points in time; for at least two data portions from one or multiple data packets determined by a selected data packet type in a data stream section, ascertaining a time series of successive values of the relevant data portion, the values of the time series corresponding to the values of the relevant data portion or being a function of these values, the values of the relevant data portion each being assigned to a respective reference point in time of the respective reference points in time; carrying out a correlation method in order to ascertain, in each case, one correlation value for at least two different time series; creating a rule for the rule-based anomaly recognition method as a function of the ascertained correlation values.