Methods and systems for combating spam

• US 20050283519A1
• Filed: 06/15/2005
• Published: 12/22/2005
• Est. Priority Date: 06/17/2004
• Status: Abandoned Application

• Current Assignee: Commtouch Software Ltd
• Original Assignee: Commtouch Software Ltd
• Inventors: Dbai, David, Lev, Amir, Turgeman, Yehuda
• Application Number:
  US11/155,022
• Publication Number:
  US 20050283519A1
• 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

A system and method for combating spam, the method including performing bulk transmission detection on incoming messages, performing characteristic-based classification on at least one incoming message and employing results of both the bulk transmission detection and the characteristic-based classification for filtering at least one incoming message.

Claims

1. A method for combating spam comprising:

performing bulk tranmission detection on incoming messages;

performing characteristic -based classification on at least one incoming message; and

employing results of both said bulk transmission detection and said characteristic -based classification for filtering at least one incoming message.

2. A method for combating spam according to claim 1 and wherein said filtering incoming messages operates on at least one incoming message which is at least partially different from said incoming messages on which said bulk transmission detection is performed and said at least one incoming message on which said characteristic -based classification is performed.

3. A method for combating spam according to claim 1 and wherein said performing bulk transmission detection is performed on first incoming messages;

said performing characteristic-based classification is performed on at least one second incoming message; and

said filtering is performed on at least one third incoming message, wherein said at least one third incoming message is at least partially different from at least one of said first incoming messages and said at least one second incoming message.

4. A method for combating spam according to claim 1 and wherein said performing bulk transmission detection and said performing characteristic classification employ at least some of the same characteristics.

5. A method for combating spam according to claim 1 and wherein said performing characteristic-based classification comprises a training functionality.

6. A method for combating spam according to claim 5 and wherein said training functionality employs at least some of said results of said performing bulk transmission detection.

7. A method for combating spam according to claim 1 and wherein at least some of said results of said characteristic-based classification are employed in said bulk transmission detection.

8. A method for combating spam according to claim 7 and wherein said results of said characteristic -based classification are employed for distinguishing between different categories of bulk transmissions.

9. A method for combating spam according to claim 7 and wherein said results of said characteristic -based classification are employed for distinguishing between solicited and non-solicited bulk transmissions.

10. A method for combating spam according to claim 1 and wherein said characteristic -based classification employs Bayesian probability models.

11. A method for combating spam according to claim 1 and wherein said performing bulk transmission detection comprises classifying a message at least partially by evaluating at least one message parameter, using at least one variable criterion, thereby providing a spam classification.

12. A method for combating spam according to claim 11 and wherein said at least one variable criterion comprises a criterion which changes over time.

13. A method for combating spam according to claim 11 and wherein said at least one variable criterion comprises a parameter template-defined function.

14. A method for combating spam according to claim 1 and wherein said filtering comprises:

evaluating incoming messages at at least one gateway; and

providing spam classifications at at least one server, receiving evaluation outputs from said at least one gateway and providing said spam classifications to said at least one gateway.

15. A method for combating spam according to claim 14 and wherein said receiving evaluation outputs comprises transmitting encrypted information from said at least one gateway to said at least one server.

16. A method for combating spam according to claim 15 and wherein said transmitting encrypted information comprises encrypting at least part of said evaluation output employing a non-reversible encryption algorithm so as to generate said encrypted information at said at least one gateway.

17. A method for combating spam according to claim 15 and wherein said transmitting comprises transmitting information of a length limited to a predefined threshold.

18. A method for combating spam according to claim 1 and wherein said filtering at least one incoming message comprises at least one of:

forwarding said message to an addressee of said message;

storing said message in a predefined storage area;

deleting said message;

rejecting said message;

sending said message to an originator of said message; and

delaying said message for a period of time and thereafter re-classifying said message.

19. A method for combating spam according to claim 1 and wherein said incoming messages comprise at least one of:

an e-mail;

a network packet;

a digital telecom message; and

an instant messaging message.

20. A method for combating spam according to claim 1 and wherein said filtering also comprises at least one of:

requesting feedback from an addressee of said message;

evaluating compliance of said message with a predefined policy;

evaluating registration status of at least one registered address in said message;

analyzing a match among network references in said message;

analyzing a match between at least one translatable address in said message and at least one other network reference in said message;

at least partially actuating an unsubscribe feature in said message;

analyzing an unsubscribe feature in said message;

employing a variable criteria;

sending information to a server and receiving classification data based on said information;

employing classification data received from a server; and

employing stored classification data.