Sony Corporation

Sony Group Corporation

Erbin Lim

Francesco Cartella

Barbara Jochems

Conor Aylward

Olivier Elshocht

Cristian Traum

Gert Ceulemans

Gabriel Armelin

Francisco Fatore

Bart Vandezande

Orlando Anunciacao

Johan Duyshaver

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

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 ().

Methods and devices for user authorization

The present disclosure relates to a concept of fraud handling. A data transaction request is received via a data network from at least one user account. The data transaction request is analyzed based on predefined fraud detection rules to generate a fraud score associated with the at least one user account. The at least one user account is classified as fraudulent account if the associated fraud score exceeds a predefined fraud likelihood threshold. Data transactions associated with a classified fraudulent account via the data network are pseudo-randomly permitted or blocked.

Methods and apparatuses for fraud handling

The present disclosure relates to a concept of fraud handling. A data transaction request is received via a data network from at least one user account. The data transaction request is analyzed based on predefined fraud detection rules to generate a fraud score associated with the at least one user account. The at least one user account is classified as fraudulent account if the associated fraud score exceeds a predefined fraud likelihood threshold. Data transactions associated with a classified fraudulent account via the data network are done by purposely deteriorating the data transactions associated with the classified fraudulent account in comparison to data transactions associated with a classified non-fraudulent account.

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Stuttgart, Germany

Erbin Lim