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 for training a first neural network is disclosed. The neural network is configured to ascertain, based on sensor signals of a technical system, an output signal characterizing a classification and/or a regression result regarding the sensor signal. The method includes (i) during operation of the technical system, receiving a sensor signal of the technical system, (ii) ascertaining a first output signal by way of the first neural network and based on the sensor signal, (iii) ascertaining a second output signal by way of a second neural network and based on the sensor signal, wherein the second neural network has a different architecture than the first neural network, and (iv) training the first neural network by adjusting parameters of the first neural network, wherein the first neural network is trained as a function of the second output signal.

Abstract: A method for training a first neural network is disclosed. The neural network is configured to ascertain, based on sensor signals of a technical system, an output signal characterizing a classification and/or a regression result regarding the sensor signal. The method includes (i) during operation of the technical system, receiving a sensor signal of the technical system, (ii) ascertaining a first output signal by way of the first neural network and based on the sensor signal, (iii) ascertaining a second output signal by way of a second neural network and based on the sensor signal, wherein the second neural network has a different architecture than the first neural network, and (iv) training the first neural network by adjusting parameters of the first neural network, wherein the first neural network is trained as a function of the second output signal.

Abstract: A method for manipulation detection of a technical device, i.e., an exhaust gas after treatment device in a motor vehicle, including: providing an input vector including system variable(s) and including at least one control variable for an intervention in the technical device for successive time steps; using a data-based manipulation detection model to generate a corresponding output vector as a classification vector in each time step for each input vector, each output vector indicates a classification of a monitored variable in value ranges, for the input vector; providing an actual monitored variable based on at least one measured value in the successive time steps; creating a measurement classification vector from the actual monitored variable for each time step; detecting a manipulation as a function of the measurement classification vector and a first and a second comparison vector for time step(s) of the time window.

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 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.