Phonetic filtering of undesired email messages

1. A method comprising:

training an email system for determining spam, where training includes at least the following:

retrieving a first email message;

generating a phonetic equivalent of at least one word from a body portion of the email message;

tokenizing the phonetic equivalent of the word to generate a token representative of the phonetic equivalent;

tokenizing at least one word in a subject line of the first email message;

tokenizing at least one simple mail transfer protocol (SMTP) email address associated with the first email message;

tokenizing at least one domain name associated with the first email message;

tokenizing at least one attachment of the first email message, wherein tokenizing the at least one attachment includes in generating a 128-bit MD5 hash of the attachment, appending a 32-bit length of the attachment to the, generated MD5 hash resulting in a 160-bit number, and UUencoding the resulting 160-bit number;

determining a spam probability from the generated tokens;

in response to a determination that the spam probability from the generated, tokens indicates that the first email message is likely spam:

determining whether the, generated tokens are present in a database of tokens;

in response to a determination that at least one of the generated tokens is not present in the database of tokens, assigning a probability value for each token as spam and adding the token and assigned probability value to the database of tokens; and

in response to a determination that the token is present in the database of tokens, updating a probability value of the token;

in response to a determination that the spam probability from the generated tokens indicates that the first email message is not likely spam;

determining whether the generated tokens are present in a database of tokens;

in response to a determination that at least one of the, generated tokens is not present in the database of tokens, assigning a probability value for each token indicative of non-spam and adding the token and assigned probability value to the database of tokens; and

in response to a determination that the token is present in the database of tokens, updating a probability value of the token;

sorting the generated tokens in accordance with the corresponding determined spam probability value; and

filtering a second email message according to the training.

2. The method of claim 1 , wherein generating the phonetic equivalent of the word comprises:

identifying a string of characters, the string of characters including a non-alphabetic character; and removing the non-alphabetic character from the string of characters.

3. The method of claim 2 , wherein removing the non-alphabetic character comprises:

locating a non-alphabetic character within the string of characters, the non-alphabetic character being at least one selected from the group consisting of:

“(quote);

‘(single quote);

!(exclamation mark);

@(at); #(pound); $(dollar);

% (percent);

^(caret);

&(ampersand);

*(asterisk);

((open parenthesis);

)(close parenthesis);

_(underscore);

-(hyphen);

+(plus);

=(equal);

\(backslash);

/(slash);

? (question mark);

(space);

(tab);

[(open square bracket);

](close square bracket);

{(open bracket);

}(close bracket);

<(less than);

>(greater than);

,(comma);

:(colon);

;(semi-colon); and

.(period).

4. The method of claim 1 , wherein determining the spam probability comprises:

assigning a spam probability value to the token; and

generating a Bayesian probability value using the spam probability value assigned to the token.

5. The method of claim 4 , wherein determining the spam probability further comprises: comparing the generated Bayesian probability value with a predefined threshold value.

6. The method of claim 5 , wherein determining the spam probability further comprises: categorizing the email message as spam in response to the Bayesian probability value being greater than the predefined threshold.

7. The method of claim 5 , wherein determining the spam probability further comprises: categorizing the email message as non-spam in response to the Bayesian probability value being not greater than the predefined threshold.

8. A training email system for determining spam on a computer storage medium comprising:

means for receiving an email message having a word and an attachment;

means for generating a phonetic equivalent of at least one word from a body portion of the email message;

means for tokenizing the phonetic equivalent of the word to generate a token representative of the phonetic equivalent;

means for tokenizing at least one word in a subject line of the first email message;

means for tokenizing at least one word in a subject line of the first email message; tokenizing at least one simple mail transfer protocol (SMTP) email address associated with the first email message;

means for tokenizing at least one domain name associated with the first email message;

means for tokenizing at least one attachment of the first email message, wherein tokenizing the at least one attachment includes in generating a 128-bit MD5 hash of the attachment, appending a 32-bit length of the attachment to the, generated MD5 hash resulting in a 160-bit number and UUencoding the resulting 160-bit number;

means for determining a spam probability from the generated tokens;

in response to a determination that the spam probability from the generated tokens, means for indicating that the first email message is likely spam:

means for determining whether the generated tokens are present in a database of tokens;

means for, in response to a determination that at least one of the generated tokens is not present in the database of tokens, assigning a probability value for each token as spam and adding the token and assigned probability value to the database of tokens;

means for, in response to a determination that the token is present in the database of tokens, updating a probability value of the token; and

means for, in response to a determination that the spam probability from the generated tokens indicates that the first email message is not likely spam:

determining whether the generated tokens are present in a database of tokens;

in response to a determination that at least one of the generated tokens is not present in the database of tokens, assigning a probability value for each token indicative of non-spam and adding the token and assigned probability value to the database of tokens; and

in response to a determination that the token is present in the database of tokens, updating a probability value of the token; sorting the generated tokens in accordance with the corresponding determined spam probability value; and filtering a second email message according to the training.

9. A system comprising:

a processor; and

a memory, the memory storing;

receive logic configured to receive an email message having a word and an attachment;

phonetic logic configured to generate a phonetic equivalent of the word from the email message;

first tokenize logic configured to tokenize the phonetic equivalent of the word to generate a token representative of the phonetic equivalent; and

second tokenize logic configured to tokenize the attachment;

tokenizing at least one word in a subject line of the first email message;

tokenizing at least one simple mail transfer protocol (SMTP) email address associated with the first email message;

tokenizing at least one domain name associated with the first email message;

tokenizing at least one attachment of the first email message, wherein tokenizing the at least one attachment includes in generating a 128-bit MD5 hash of the attachment, appending a 32-bit length of the attachment to the, generated MD5 hash resulting in a 160-bit number, and UUencoding the resulting 160-bit number;

determining a spam probability from the generated tokens;

in response to a determination that the spam probability from the generated tokens indicates that the first email message is likely spam:

determining whether the generated tokens are present in a database of tokens;

in response to a determination that at least one of the generated tokens is not present in the database of tokens, assigning a probability value for each token as spam and adding the token and assigned probability value to the database of tokens;

in response to a determination that the token is present in the database of tokens, updating a probability value of the token; and

in response to a determination that the spam probability from the generated tokens indicates that the first email message is not likely spam;

determining whether the generated tokens are present in a database of tokens;

in response to a determination that at least one of the generated tokens is not present in the database of tokens, assigning a probability value for each token indicative of non-spam and adding the token and assigned probability value to the database of tokens; and

in response to a determination that the token is present in the database of tokens, updating a probability value of the token; sorting the, generated tokens in accordance with the corresponding determined spam probability value; and

filtering a second email message according to the training.

10. The system of claim 9 , the memory further storing;

string-identification logic configured to identify a string of characters, the string of characters including a non-alphabetic character; and

character-removal logic configured to remove the non-alphabetic character from the string of characters.

11. The system of claim 10 , the memory further storing; spam-probability logic configured to assign a spam probability value to the token; and Bayesian logic configured to generate a Bayesian probability value using the spam probability value assigned to the token.

12. The system of claim 11 , the memory further storing; compare logic configured to compare the generated Bayesian probability value with a predefined threshold value.

13. The system of claim 12 , the memory further storing; spam-categorization logic configured to categorize the email message as spam in response to the Bayesian probability value being greater than the predefined threshold.

14. The system of claim 12 , the memory further storing; spam-categorization logic configured to categorize the email message as non-spam in response to the Bayesian probability value being not greater than the predefined threshold.

15. A computer-readable medium that includes a program that, when executed by a computer, causes the computer to perform at least the following:

receive an email message having a word and an attachment;

generate a phonetic equivalent of the word from the email message;

tokenize the phonetic equivalent of the word to generate a token representative of the phonetic equivalent;

tokenize the attachment;

generate a phonetic equivalent of at least one word from a body portion of the email message;

tokenize the phonetic equivalent of the word to generate a token representative of the phonetic equivalent;

tokenize at least one word in a subject line of the first email message;

tokenizing at least one simple mail transfer protocol (SMTP) email address associated with the first email message;

tokenize at least one domain name associated with the first email message;

tokenize at least one attachment of the first email message, wherein tokenizing the at least one attachment includes in generating a 128-bit MD5 hash of the attachment, appending a 32-bit length of the attachment to the, generated MD5 hash resulting in a 160-bit number, and UUencoding the resulting 160-bit number;

determine a spam probability from the generated tokens;

in response to a determination that the spam probability from the generated tokens, indicate that the first email message is likely spam;

determine whether the, generated tokens are present in a database of tokens;

in response to a determination that at least one of the, generated tokens is not present in the database of tokens,

assigning a probability value for each token as spam and adding the token and assigned probability value to the database of tokens; and

in response to a determination that the token is present in the database of tokens, updating a probability value of the token; and

in response to a determination that the spam probability from the generated tokens, indicates that the first email message is not likely spam:

determining whether the generated tokens are present in a database of tokens;

in response to a determination that at least one of the, generated tokens is not present in the database of tokens, assigning a probability value for each token indicative of non-spam and adding the token and assigned probability value to the database of tokens; and

in response to a determination that the token is present in the database of tokens, update a probability value of the token;

sort the generated tokens in accordance with the corresponding determined spam probability value; and filter a second email message according to the training.

16. The computer-readable medium of claim 15 , the program further causing the computer to perform at least the following:

instruct a programmable device to identify a string of characters, the string of characters including a non-alphabetic character; and

remove the non-alphabetic character from the string of characters.

17. The computer-readable medium of claim 15 , the program further causing the computer to perform at least the following:

assign a spam probability value to the token; and

generate a Bayesian probability value using the spam probability value assigned to the token.

18. The computer-readable medium of claim 17 , the program further causing the computer to perform at least the following:

instruct a programmable device to compare the generated Bayesian probability value with a predefined threshold value.

19. The computer-readable medium of claim 18 , the program further causing the computer to perform at least the following:

categorize the email message as spam in response to the Bayesian probability value being greater than the predefined threshold.

20. The computer-readable medium of claim 18 , the program further causing the computer to perform at least the following:

categorize the email message as non-spam in response to the Bayesian probability value being not greater than the predefined threshold.