SPAM IDENTIFICATION SYSTEM

• US 20090282112A1
• Filed: 05/12/2008
• Published: 11/12/2009
• Est. Priority Date: 05/12/2008
• Status: Abandoned Application

• Current Assignee: Cloudmark Incorporated (Proofpoint Incorporated)
• Original Assignee: Cloudmark Incorporated (Proofpoint Incorporated)
• Inventors: Prakash, Vipul Ved
• Application Number:
  US12/119,429
• Publication Number:
  US 20090282112A1
• Time in Patent Office: Days
• US Class Current: 709/206
• CPC Class Codes: G06Q 10/107 Computer-aided management of electronic mailing [e-mailing]
• CPC Class Codes: H04L 51/212 using filtering or selective blocking

Abstract

In one embodiment, a method includes receiving a plurality of reports from a community of users, each of the plurality of reports including a derivative of an email message to uniquely identify the email message, and suggesting the email message as being spam. The e-mail message is determined to be spam based on the plurality of reports received from the community of users, and a respective trust factor associated with each user of the community of users.

Claims

1. A method, comprising:

receiving a plurality of reports from a community of users, each of the plurality of reports;

including a derivative of an email message to uniquely identify the email message, and

suggesting the email message as being spam; and

determining the email message to be spam based on the plurality of reports received from the community of users, and a respective trust factor associated with each user of the community of users.

2. The method of claim 1, further comprising maintaining a database of email messages determined to be spam.

3. The method of claim 2, further comprising providing notifications of email messages stored in the database and determined to be spam.

4. The method of claim 3, wherein each notification is in response to a request received from a further user for an indication of whether an identified message is spam.

5. The method of claim 1, wherein the trust factor associated with a respective user of the community of users is based on an indication of how accurately previous reports, received from the respective user, identified email messages as spam.

6. The method of claim 5, including determining the accuracy of the previous reports, received from the respective user, based on a degree of consensus between the users of the community as to what email messages are spam.

7. The method of claim 1, wherein the derivative of the email message comprises a hash calculated based on the content of the email message.

8. The method of claim 2, further comprising:

using the derivative of the email message as a key to store the email message in the database.

9. A server, comprising:

a processor; and

a memory coupled to the processor, the memory storing instructions which when executed by the processor cause the processor to perform a method comprising;

receiving a plurality of reports from a community of users, each of the plurality of reports;

including a derivative of an email message to uniquely identify the email message, and

suggesting the email message as being spam; and

determining the email message to be spam based on the plurality of reports received from the community of users, and a respective trust factor associated with each user of the community of users.

10. The server of claim 9, wherein the method further comprises maintaining a database of email messages determined to be spam.

11. The server of claim 10, wherein the method further comprises providing notification of email messages stored in the database and determined to be spam.

12. The server of claim 11, wherein each notification is in response to a request received from a further user for an indication on whether an identified message is spam.

13. The server of claim 9, wherein the trust factor associated with a respective user of the community of users is based on an indication of how accurately previous reports, received from the respective user, identified email messages as spam

14. The server of claim 13, wherein the method includes determining the accuracy of the previous reports, received from the respective user, based on agreement between the users of the community as to what email messages are spam

15. The server of claim 9, wherein the derivative of the email message comprises a hash calculated based on the content of the email message.

16. The server of claim 10, wherein the method further comprises:

using the derivative of the email message as a key to store the email message in the database.

17. A machine-readable medium having stored thereon instructions which, when executed by a computer, cause the computer to perform a method comprising:

receiving a plurality of reports from a community of users, each of the plurality of reports;

including a derivative of an email message to uniquely identify the email message, and

suggesting the email message as being spam; and

determining the email message to be spam based on the plurality of reports received from the community of users, and a respective trust factor associated with each user of the community of users.

18. The machine-readable medium of claim 17, wherein the method further comprises maintaining a database of email messages determined to be spam.

19. The machine-readable medium of claim 18, wherein the method further comprises providing notifications of email messages stored in the database and determined to be spam.

20. The machine-readable medium of claim 19, wherein each notification is in response to a request received for an indication on whether an identified message is spam.

21. The machine-readable medium of claim 17, wherein the trust factor associated with a respective user of the community of users is based on an indication of how accurately previous reports, received from the respective user, identified email messages as spam.

22. The machine-readable medium of claim 21, wherein the determining of the accuracy of the previous reports, received from the respective user, is based on a degree of consensus between the users of the community as to what email messages are spam.

23. The machine-readable medium of claim 17, wherein the derivative of the email message comprises a hash calculated based on the content of the email message.

24. The machine-readable medium of claim 18, wherein the method further comprises:

using the derivative of the email message as a key to store the email message iii the database.

25. A system comprising:

first means for receiving a plurality of reports from a community of users, each of the plurality of reports;

including a derivative of an email message to uniquely identify the email message, and

suggesting the email message as being spam; and

second means for determining the email message to be spam based on the plurality of reports received from the community of users, and a respective trust factor associated with each user of the community of users.

26. The system of claim 25, wherein the trust factor associated with a respective user of the community of users is based on an indication of how accurately previous reports, received from the respective user, identified email messages as spam.

27. The system of claim 25, wherein the second means is for determining the accuracy of the previous reports, received from the respective user, based on a degree of consensus between the users of the community as to what email messages are spam.