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 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: The present disclosure proposes a method for supporting a user. The method contains providing an acceleration measurement of the user and measuring at least one vital parameter of the user if the acceleration measurement is above a predefined acceleration threshold. A need of assistance for the user is signaled if the measured vital parameter of the user deviates from a standard vital parameter of the user by more than a predefined threshold.

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.

Abstract: The present disclosure proposes a method for supporting a user. The method contains providing an acceleration measurement of the user and measuring at least one vital parameter of the user if the acceleration measurement is above a predefined acceleration threshold. A need of assistance for the user is signaled if the measured vital parameter of the user deviates from a standard vital parameter of the user by more than a predefined threshold.