Classification of a multi-activity dataset in a telecommunications network

Abstract

Classification of a multi-activity dataset in a telecommunications network. The invention relates to a method for classifying a dataset, comprising: - receiving (211) a dataset comprising a network trace identifying packets exchanged on a telecommunications network by at least one user during a given period; - identifying (217) a multi-activity category for the network trace, from among a predefined set of multi-activity categories by applying a prediction model to at least one characteristic of the network trace determined (216) from the packets identified in the network trace. Figure for the abstract: [Fig 2b]

Claims

Claims

1. Method for classifying a data set, comprising the following steps: - receiving (211) a data set comprising at least one network trace, the network trace identifying packets exchanged on a telecommunications network during a given duration; - identifying (217) a multi-activity category for the network trace, from a predefined set of multi-activities, by applying a prediction model to at least one characteristic of the network trace determined (216) from the packets identified in the network trace, each multi-activity category identifying an implementation of at least two digital activities.

2. The method of claim 1, further comprising fragmenting (212) the network trace of the data set into at least one fragment of predetermined duration T, wherein said at least one feature is extracted (216) of said at least one fragment from the packets identified in said fragment, and wherein the multi-activity category is identified for the network trace by applying the model to said at least one feature extracted from said at least one fragment.

3. The method of claim 2, wherein said at least one fragment is divided into N samples of equal duration T / N, N being an integer greater than or equal to two, wherein at least one feature is extracted for each sample from the packets identified in said sample, and wherein the multi-activity category is identified for the network trace by applying the model to the features extracted from the N samples of said at least one fragment.

4. A method according to claim 2 or 3, comprising, for each fragment, a verification (213) that said at least one fragment is a multi-activity fragment, and wherein said at least one characteristic is extracted from said at least one fragment if and only if said at least one fragment is a multi-activity fragment.

5. Method according to one of the preceding claims, wherein said at least one extracted characteristic (216) from identified packets comprises one or a combination of the following characteristics: - mean, variance, asymmetry coefficient, acuity coefficient of packet sizes and sum of packet sizes; - mean, variance, asymmetry coefficient, acuity coefficient of in- packet arrival intervals of the training sample; - number of packets.

6. Method according to one of the preceding claims, comprising a preliminary phase (200-207) of training the model by supervised learning, from a set of training data sets, each training data set being associated in a first database (111) with a label identifying a multi-activity category from the predefined set of multi-activity categories.

7. Method according to claim 6, wherein the preliminary training phase comprises the following steps: - obtaining (201) a first training data set comprising at least one training network trace, said first training data set being associated with a first multi-activity category from the predefined set of multi-activity categories; - determining (203) at least one characteristic of the training network trace from packets identified in the training network trace; - training (204) the model by comparison between a prediction of an obtained multi-activity category identified from said at least one characteristic of the training network trace and the first multi-activity category associated with the first training data set.

8. Method according to one of the preceding claims, in which the trained model is one of the following models: - a random forest type model; - an eXtreme Gradient Boosting model, or XGBoost; - a one-dimensional convolutional neural network, with attention mechanism; - a bidirectional long short-term memory cell, BiLSTM, for "Bidirectional Long Short-Term Memory, with attention mechanism; - a Transformer type encoder.

9. Classification device (110) capable of accessing or storing a prediction model of a multi-activity category from a predefined set of multi-activity categories, based on at least one characteristic received as input to the model, each multi-activity category identifying an alternating or simultaneous implementation of at least two digital activities, the classification device comprising in furthermore: - a first interface (303) capable of receiving a data set comprising at least one network trace, the network trace identifying packets exchanged on a telecommunications network by at least one user during a given duration; - a processor (301) configured to identify a multi-activity category for the network trace by applying the prediction model to at least one characteristic of the network trace determined from the packets identified in the network trace.

10. System comprising a classification device according to claim 9 and a user terminal capable of implementing at least two digital activities, the data set received by the classification device identifying packets received and / or transmitted by the user terminal.

11. A computer program implementable in a classification device as defined in claim 9, the program comprising code instructions which, when executed by a processor (301), performs the steps of the method defined in one of claims 1 to 8.

12. A computer-readable recording medium on which at least one series of program code instructions for executing the method according to one of claims 1 to 8 is recorded.

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