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1. WO2020194323 - PROCÉDÉ ET SYSTÈME DE DÉTECTION DE POURRIEL PAR MESSAGE DANS DES RÉSEAUX DE COMMUNICATION

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[ EN ]

CLAIMS:

1. A method performed by a network node for spam detection in a communication network, the method comprising:

receiving (502), from a first device of a first sender, a message addressed to one or more receivers;

determining (504) a set of features for the message, wherein the set of features includes at least one or more content features that relates to content of the message, and one or more user features that relate to the first sender;

determining (506), based on a spam prediction model and the set of features for the message, whether the first sender is a spam sender and whether the message includes spam content;

responsive to determining that at least one of the first sender is a spam sender and the message includes spam content, transmitting (508) a notification to one or more devices of the one or more receivers including the at least one of a user flag and a message flag, wherein the user flag indicates that the first sender is a spam user and the message flag indicates that the message includes spam content; and

responsive to determining that the first sender is not a spam sender and the message does not include spam content, transmitting (512) the message to the one or more devices of the one or more receivers.

2. The method of claim 1, wherein the notification includes (510) the message.

3. The method of any of claims 1-2, wherein the at least one of a user flag and a message flag includes (602) the user flag only when the user flag indicates that the first sender is a spam sender and the message flag does not indicate that the message includes spam content.

4. The method of any of claims 1-2, wherein at least one of a user flag and a message flag includes (604) the message flag only when the message flag indicates that the message includes spam content and the user flag does not indicate that the first sender is a spam user.

5. The method of any of claims 1-2, wherein the at least one of a user flag and a message flag include (606) the message flag and the user flag when the message flag

indicates that the message includes spam content and the user Hag indicates that the first sender is a spam user.

6. The method of any of claims 1-5, further comprising:

receiving (702), from a second device of the one or more devices, spam feedback that indicates whether a second user of the second device agrees with the at least one of the user flag and the message flag; and

using (704) the spam feedback as a feature for training the spam prediction model to obtain an updated spam prediction model that is to be used for determining, for new messages received, whether senders and message contents are respectively spam users or include spam content.

7. The method of claim 6, wherein the spam feedback includes at least one of a first indication that the second user agrees that the first sender is a spam user and a second indication that the second user agrees that the message includes spam content.

8. The method of any of claims 1-7, further comprising:

receiving (802), from a third device, second spam feedback that indicates at least one of whether a second sender of a second message is a spam user and whether the second message received at the third device includes spam content; and using (804) the second spam feedback as a feature for training the spam prediction model to obtain an updated spam prediction model that is to be used for determining, for new messages received, whether senders and message contents are respectively spam users or include spam content.

9. The method of claim 8, wherein the second spam feedback includes at least one of a third indication that the second sender is a spam user and a fourth indication that the second message includes spam content.

10. The method of any of claims 1-8, wherein the message includes one or more of text, image, video, and a hyperlink.

11. The method of any of claims 1-10, wherein the one or more devices of the one or more receivers are located at a same location and the spam prediction model is determined for the location.

12. A machine-readable medium comprising computer program code which when executed by a computer carries out the method steps of any of claims 1-11.

13. A network node for spam detection in a communication network, the network node including:

one or more processors; and

non-transitory computer readable storage media that stores instructions, which when executed by the one or more processors cause the network node to:

receive (502), from a first device of a first sender, a message addressed to one or more receivers,

determine (504) a set of features for the message, wherein the set of features includes at least one or more content features that relates to content of the message, and one or more user features that relate to the first sender,

determine (506), based on a spam prediction model and the set of features for the message, whether the first sender is a spam sender and whether the message includes spam content,

responsive to determining that at least one of the first sender is a spam sender and the message includes spam content, transmit (508) a notification to one or more wireless devices of the one or more receivers including the at least one of a user flag and a message flag, wherein the user flag indicates that the first sender is a spam user and the message flag indicates that the message includes spam content, and responsive to determining that the first sender is not a spam sender and the message does not include spam content, transmit (512) the message to the one or more devices of the one or more receivers,

14. The network node of claim 13, wherein the notification includes (510) the message.

15. The network node of any of claims 13-14, wherein the at least one of a user flag and a message flag includes (602) the user flag only when the user flag indicates that the first sender is a spam sender and the message flag does not indicate that the message includes spam content.

16. The network node of any of claims 13-14, wherein at least one of a user flag and a message flag includes (604) the message flag only when the message flag indicates that the message includes spam content and the user flag does not indicate that the first sender is a spam user,

17. The network node of any of claims 13-14, wherein the at least one of a user flag and a message flag include (606) the message flag and the user flag when the message flag indicates that the message includes spam content and the user flag indicates that the first sender is a spam user.

18. The network node of any of clai s 13-17, wherein the instructions when executed by the one or more processors are further to cause the network node to:

receive (702), from a second wireless device of the one or more devices, spam feedback that indicates whether a second user of the second device agrees with the at least one of the user flag and the message flag; and use (704) the spam feedback as a feature for training the spam prediction model to obtain an updated spam prediction model that is to be used for determining, for new messages received, whether senders and message contents are respectively spam users or include spam content.

19. The network node of claim 18, wherein the spam feedback includes at least one of a first indication that the second user agrees that the first sender is a spam user and a second indication that the second user agrees that the message includes spam content.

20. The network node of any of claims 13-19, wherein the instructions when executed by the one or more processors are further to cause the network node to:

receive (802), from a third device, second spam feedback that indicates at least one of whether a second sender of a second message is a spam user and whether the second message received at the third device includes spam content; and use (804) the second spam feedback as a feature for training the spam prediction model to obtain an updated spam prediction model that is to be used for determining, for new messages received, whether senders and message contents are respectively spam users or include spam content.

21. The network node of claim 20, wherein the second spam feedback includes at least one of a third indication that the second sender is a spam user and a fourth indication that the second message includes spam content.

22. The network node of any of claims 13-21, wherein the message includes one or more of text, image, video, and a hyperlink.

23. The network node of any of claims 13-22, wherein the one or more devices of the one or more receivers are located at a same location and the spam prediction model is determined for the location.