Sony Corporation

Sony Group Corporation

Francesco Cartella

Erbin Lim

Gert Ceulemans

Orlando Anunciacao

Gabriel Armelin

Barbara Jochems

Olivier Elshocht

Francisco Fatore

Conor Aylward

Cristian Traum

Johan Duyshaver

Ales Novak

Karin Cvetko Vah

A method for defending a predetermined area from an autonomously moving Unmanned Aerial Vehicle (UAV) is provided. The method includes generating one or more adversarial example adapted to disrupt a machine-learning based vision system of the UAV. Additionally, the method includes determining, based on geographical information about at least one of the predetermined area and a surrounding area of the predetermined area, a respective position for the one or more adversarial example in at least one of the predetermined area and the surrounding area of the predetermined area.

Apparatus and method for defending a predetermined area from an autonomously moving unmanned aerial vehicle

Embodiments of the present disclosure relate to a method, an apparatus and a computer program for providing a machine-learning system to be used for classifying transactions as either fraudulent or genuine, and a method, an apparatus and a computer program for classifying transactions as either fraudulent or genuine. The method for generating a machine-learning system for classifying transactions as either fraudulent or genuine based on a plurality of training transactions, each training transaction being associated with labelling information that indicates whether the training transaction is either genuine or fraudulent, comprises clustering the plurality of training transactions into a plurality of clusters based on a similarity measure. The method comprises determining, for each of the plurality of clusters, whether the cluster is homogeneous or heterogeneous. A heterogeneous cluster includes both fraudulent and genuine training transactions. A homogeneous cluster includes either only fraudulent or only genuine training transactions. The method comprises, for each of the heterogeneous clusters, training an associated machine-learning model using the training transactions belonging to the respective heterogeneous cluster.

Method, apparatus and computer programs for generating a machine-learning system and for classifying a transaction as either fraudulent or genuine

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.

Methods for detecting a cyberattack on an electronic device, method for obtaining a supervised random forest model for detecting a DDoS attack or a brute force attack, and electronic device configured to detect a cyberattack on itself

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.

Method and apparatus for generating a combined isolation forest model for detecting anomalies in data

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.

Method, apparatus and computer program for selecting a subset of training transactions from a plurality of training transactions

The disclosure relates to a method () for assessing user authorization, the method comprising: receiving (), via a data communication network (), a request from a user device () for an access; generating (), based on data associated with the request, a risk score indicating a risk that the request was sent by a non-authorized user, wherein the risk score indicates a high risk, a medium risk, or a low risk that the user () is a non-authorized user; and signaling (), via the data communication network (), the user device () a need for further information to enable a decision about the authorization of the user (), if the risk score indicates medium risk. A further aspect relates to a method () for user authorization and to an electronic device ().

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Stuttgart, Germany

Francesco Cartella