Fraudelent subscription detection

1. A method for identifying a second fraudulent subscription replacing a first fraudulent subscription, the method being performed by a fraudulent subscription detection system, the method comprising:

obtaining notification of the first fraudulent subscription having been identified in a SIM box;

obtaining historical network data of the first fraudulent subscription;

generating a model based on the historical network data;

identifying the second fraudulent subscription replacing the first fraudulent subscription in the SIM box upon providing live network data as input to the model, identifying the second fraudulent subscription comprising:

generating, using the model, a set of candidate subscriptions;

determining an individual score for each candidate subscription in the set of candidate subscriptions, each individual score being generated from the live network data, each individual score relating to each candidate subscription in the set of candidate subscriptions corresponding to whether the candidate subscription in the set of candidate subscriptions is a fraudulent subscription;

comparing a highest individual score of all of the individual scores relating to each candidate subscription in the set of candidate subscriptions to a first threshold; and

the second fraudulent subscription being identified as a candidate subscription from the set of candidate subscriptions having the highest individual score that is above the first threshold; and

providing an identification of the second fraudulent subscription to at least one of a subscription manager entity and a user interface of a Manual Analysis component.

2. The method according to claim 1 , wherein the historical network data is used to generate the model to have characteristics of the first fraudulent subscription.

3. The method according to claim 2 , wherein the characteristics pertain to at least one of mobile equipment identifier, subscription identifier, geographical location, and calling pattern of the first fraudulent subscription.

4. The method according to claim 1 , wherein identifying the second fraudulent subscription involves comparing feature vectors of any subscriptions generated from the live network data with a feature vector built from the historical network data for the first fraudulent subscription.

5. The method according to claim 4 , wherein identifying the second fraudulent subscription involves classifying, according to the comparing, each of the any subscriptions as one of legitimate and fraudulent.

6. The method according to claim 1 , wherein, when the highest individual score of all of the individual scores relating to each candidate subscription in the set of candidate subscriptions is not above the first threshold, identifying the second fraudulent subscription further comprises:

comparing the highest individual score relating to each candidate subscription in the set of candidate subscriptions to a second threshold, the second threshold being lower than the first threshold; and

obtaining, when the highest individual score relating to each candidate subscription in the set of candidate subscriptions is higher than the second threshold, manual input from the user interface of the Manual Analysis component for identifying the second fraudulent subscription as one of the subscriptions having its individual score above the second threshold.

7. The method according to claim 1 , wherein a first timer is started upon obtaining the notification, and wherein the second fraudulent subscription is identified before expiration of the first timer.

8. The method according to claim 1 , wherein a second timer is started upon obtaining the notification, and wherein when the second fraudulent subscription is identified before expiration of the second timer, the model is kept for identifying a third fraudulent subscription replacing the second fraudulent subscription.

9. The method according to claim 1 , wherein a second timer is started upon obtaining the notification, and wherein when the second fraudulent subscription is not identified before expiration of the second timer, a new model is generated for identifying a third fraudulent subscription replacing the second fraudulent subscription.

10. The method according to claim 1 , wherein the notification is obtained from a SIM box detector.

11. The method according to claim 10 , wherein the SIM box detector utilizes at least one of a test call generator service, a fraud management system, and a machine learning, ML, algorithm to identify the first fraudulent subscription.

12. The method according to claim 1 , wherein the historical network data represents at least one of a call detail record, a customer relationship management record, and mobile network data of the first fraudulent subscription.

13. The method according to claim 1 , wherein a set of subscriptions are generated by the fraudulent subscription detection system, and wherein the live network data represents at least one of a call detail record, a customer relationship management record, and mobile network data of the set of subscriptions.

14. The method according to claim 13 , wherein the live network data comprises initial signaling for setting up the set of subscriptions.

15. The method according to claim 1 , further comprising:

forwarding the notification of the first fraudulent subscription to the subscription manager entity.

16. A fraudulent subscription detection system for identifying a second fraudulent subscription replacing a first fraudulent subscription, the fraudulent subscription detection system comprising processing circuitry, the processing circuitry being configured to cause the fraudulent subscription detection system to:

obtain notification of the first fraudulent subscription having been identified in a SIM box;

obtain historical network data of the first fraudulent subscription;

generate a model based on the historical network data;

identify the second fraudulent subscription replacing the first fraudulent subscription in the SIM box upon providing live network data as input to the model, identifying the second fraudulent subscription comprising:

generating, using the model, a set of candidate subscriptions;

determining an individual score for each candidate subscription in the set of candidate subscriptions, each individual score being generated from the live network data, each individual score relating to each candidate subscription in the set of candidate subscriptions corresponding to whether the candidate subscription in the set of candidate subscriptions is a fraudulent subscription;

comparing a highest individual score of all of the individual scores relating to each candidate subscription in the set of candidate subscriptions to a first threshold; and

the second fraudulent subscription being identified as a candidate subscription from the set of candidate subscriptions having the highest individual score that is above the first threshold; and

provide an identification of the second fraudulent subscription to at least one of a subscription manager entity and a user interface of a Manual Analysis component.