Abstract: A method for generating a combined Isolation Forest model for detecting anomalies in data is provided. The method includes receiving a first Isolation Forest model for detecting anomalies in data from a first electronic device. The first Isolation Forest model is trained at the first electronic device. Further, the method includes receiving a second Isolation Forest model for detecting anomalies in data from a second electronic device. The second Isolation Forest model is trained at the second electronic device. The method additionally includes combining the first Isolation Forest model and the second Isolation Forest model to obtain the combined Isolation Forest model.

Abstract: Training a machine learning model includes selecting a subset of training transactions from a plurality of training transactions to be used for classifying transactions as either fraudulent or genuine, and classifying transactions as either fraudulent or genuine. Additionally, selecting the subset of training transactions includes clustering the plurality of training transactions into a plurality of clusters based on a similarity measure. Each training transaction includes an indication whether the training transaction is fraudulent or genuine. Further, selecting the subset of training transactions includes selecting a first proportion of the training transactions of a cluster of training transactions for the subset of training transactions if the cluster comprises at least one fraudulent training transaction, and selecting a second proportion of the training transactions of a cluster of training transactions for the subset of training transactions if the cluster is free of fraudulent training transactions.

Abstract: A denoising apparatus, including a Micro-Electro-Mechanical System, MEMS, sensor circuit, which is configured to generate a measurement signal in response to a physical quantity. The measurement signal includes a useful signal component indicative of the physical quantity and an attack signal component due to an attack on the MEMS sensor circuit. The denoising apparatus further includes a machine learning circuitry, which is configured to estimate the useful signal component based on the measurement signal. The machine learning circuitry is trained based on training signals comprising known useful signal components and known attack signal components.

Abstract: A method for detecting a cyberattack on an electronic device is provided. The method is performed by the electronic device itself. The method includes collecting data at the electronic device. Further, the method includes classifying the collected data as regular data or malicious data using a supervised machine-learning model for the cyberattack. The method additionally includes determining whether the electronic device is under the cyberattack based on the classification of the collected data.

Abstract: A method for processing a user request is provided. The method includes receiving the user request. Further, the method includes selecting one of a plurality of different machine-learning models. Each of the plurality of machine-learning models is trained for performing the same processing task. The method additionally includes processing the user request using the selected one of the plurality of machine-learning models.

Abstract: A method for generating a combined Isolation Forest model for detecting anomalies in data is provided. The method includes receiving a first Isolation Forest model for detecting anomalies in data from a first electronic device. The first Isolation Forest model is trained at the first electronic device. Further, the method includes receiving a second Isolation Forest model for detecting anomalies in data from a second electronic device. The second Isolation Forest model is trained at the second electronic device. The method additionally includes combining the first Isolation Forest model and the second Isolation Forest model to obtain the combined Isolation Forest model.

Abstract: Embodiments of the present disclosure relate to a method, an apparatus and a computer program for selecting a subset of training transactions from a plurality of training transactions for training a machine-learning model to be used for classifying trans-actions as either fraudulent or genuine, and to a method for classifying transactions as either fraudulent or genuine. The method for selecting a subset of training transactions from a plurality of training transactions for training a machine-learning model to be used for classifying trans-actions as either fraudulent or genuine comprises clustering the plurality of training transactions into a plurality of clusters based on a similarity measure. Each training transaction comprises an indication whether the training transaction is fraudulent or genuine.