TECHNICAL FIELD

The present invention relates to a method for improving user authentication performed by a communication device. It is applicable to machine learning in a server computing environment.

BACKGROUND OF THE INVENTION

An Artificial Neural Network (ANN) is a computing system inspired by the animal brains. It is composed of a plurality of artificial neurons connected to each other's using artificial synapses. Artificial Neural Networks allow to implement machine learning algorithms having the ability to learn and improve themselves based on their experience.

A machine learning algorithm needs to be trained during a so-called learning phase. It is then operational and continues to adapt itself thanks to the reception of new stimuli.

Machine learning and artificial intelligence are considered of growing interest and these technologies can be implemented in a server computing environment.

Server computing generally refers to a network infrastructure allowing access to shared resources such as storage and applications. This encompasses what one is calling cloud computing. Server computing enables a local device, for example a personal computer connected to the internet, to access remotely to these shared resources. A user can therefore store its private data in one or several remote servers of the network infrastructure and/or benefit from an application software that is executed in the server and not in its local device.

For machine learning and artificial intelligence implemented in the server, the neural network can be permanently updated and improved thanks to the inputs of potentially an unlimited number of users using the server service.

There are existing solutions where the machine learning algorithms are implemented in the server. For example, technologies provided by Behaviosec and Unify ID are able to authenticate end user without any end user action. The machine learning technology analyses the behaviour of the user for authentication purpose.

The analysis of the user's behaviour can take into account the way the user is walking. For that purpose, a mobile application gets data from a microelectromechanical system (MEMS) and sends them to a remote server. The analysis can also take into account the characteristics of the user's sitting position. As an example, a dedicated device embedded in a seat generates data corresponding to the sitting position of the user and send it to a remote server. In addition, the way the user is driving can also be analyzed and reported to the server.

One drawback of this type of machine learning technology is that the devices which are configured to capture data need to be always online for reporting the captures data when available. This requires online connectivity which is not always available. For example, a device embedded in a car can be localized in an underground car park where online connectivity is not available.

SUMMARY OF THE INVENTION

This invention relates to a method for improving user authentication efficiency performed by a communication device belonging to an authentication system, the communication device comprising a local machine learning engine (LMLE) comprising a set of N artificial neural network ANN1,iadapted to process N different types of input signals.

The method comprises the following steps:receiving a first set of N input signals S_1(i) of different types provided by the requesting user for authentication purpose;determining respectively for each of the N input signals S_1(i) by the N artificial neural networks ANN1,i, N estimations LH(i) of the likelihood that a given input signal is provided by a legitimate user allowed to access to at least a service provided by the communication device;determining based on a risk scoring established using the N estimations LH(i) if the requesting user is authenticated as the legitimate user;if the requested user is authenticated, determining if at least one likelihood estimation determined for a given input signal S_1(j) is below a predetermined threshold Tc,i, and if it is the case:transmitting the input signal S_1(j) to a remote server implementing a server machine learning engine (SMLE) adapted to process said N different types of input signals and trained to identify a user U_C, called closest candidate, different from the requesting user and for which the transmitted input signal S_1(j) provides the best matching result, the remote server then providing an input signal S_2(j) associated to the closest candidate U_C and of the same type as S_1(j);receiving the input signal S_2(j) associated to the closest candidate U_C and executing by the local machine learning engine (LMLE) an additional learning phase using the input signal S_2(j) as an input signal that is not associated to the requesting user.

According to an example, the N artificial neural networks ANN1,iare trained during an initial learning phase applied prior to the additional learning phase at the time of setting up the access of the legitimate user to the communication device using a second set of input signals provided by the legitimate user.

According to an example, the method comprises the step applied after the initial learning phase of transmitting the second set of input signals to the server machine learning engine (SMLE) for training the server machine learning engine (SMLE).

According to an example, the second set of input signals is stored in a database where other sets of input signals provided by different users of the authentication system are also stored, these sets of input signals being used for setting up the additional learning phases of a plurality of communication devices belonging to the authentication system.

According to an example, at least one of the N input signals S_1(i) is voice signal captured by a microphone of the communication device.

According to an example, at least one of the N input signals S_1(i) is a gait signal representative of the user's gait.

According to an example, the gait signal is captured by a video camera capable of exchanging data with the communication device and installed in the user vicinity.

According to an example, at least one of the N input signals S_1(i) is a fingerprint signal captured by a fingerprint sensor implemented on the communication device.

According to an example, at least one of the N input signals S_1(i) is an environmental signals such as wireless network access point identifiers.

According to an example, the input signal S2(j) is received by the communication device without any identification information allowing the identification of the user to which it is associated.

The invention also relates to a communication device belonging to an authentication system, the communication device comprising a local machine learning engine (LMLE) comprising a set of N artificial neural network ANN1,iadapted to process N different types of input signals, the communication device being configured to:receive a first set of N input signals S_1(i) of different types provided by the requesting user for authentication purpose;determine respectively for each of the N input signals S_1(i) by the N artificial neural networks ANN1,i, N estimations LH(i) of the likelihood that a given input signal is provided by a legitimate user allowed to access to at least a service provided by the communication device;determine based on a risk scoring established using the N estimations LH(i) if the requesting user is authenticated as the legitimate user;if the requested user is authenticated, determining if at least one likelihood estimation determined for a given input signal S_1(j) is below a predetermined threshold TC,i, and if it is the case:transmit the input signal S_1(j) to a remote server implementing a server machine learning engine (SMLE) adapted to process said N different types of input signals and trained to identify a user U_C, called closest candidate, different from the requesting user and for which the transmitted input signal S_1(j) provides the best matching result, the remote server then providing an input signal S_2(j) associated to the closest candidate U_C and of the same type as S_1(j);receive the input signal S_2(j) associated to the closest candidate U_C and execute by the local machine learning engine (LMLE) an additional learning phase using the input signal S_2(j) as an input signal that is not associated to the requesting user.

The invention also relates to an authentication system comprising a communication device and a remote server comprising means for carrying out the steps of the method described above.

The invention also relates to a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method described above.

