Intelligent SPAM detection system using an updateable neural analysis engine

• US 20030088627A1
• Filed: 07/26/2001
• Published: 05/08/2003
• Est. Priority Date: 07/26/2001
• Status: Active Grant

• Current Assignee: McAfee, Inc. (McAfee, LLC)
• Original Assignee: McAfee, Inc. (McAfee, LLC)
• Inventors: Jagger, Luke D., Rothwell, Anton C., Dennis, William R., Clarke, David R.
• Granted Patent: US 6,769,016 B2
• Time in Patent Office: Days
• US Class Current: 709/206
• CPC Class Codes: H04L 51/212 using filtering or selective blocking
• CPC Class Codes: Y10S 707/99931 Database or file accessing
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation

Abstract

A system, method and computer program product are provided for detecting an unwanted message. First, an electronic mail message is received. Text in the electronic mail message is decomposed. Statistics associated with the text are gathered using a statistical analyzer. A neural network engine coupled to the statistical analyzer is taught to recognize unwanted messages based on statistical indicators. The statistical indicators are analyzed utilizing the neural network engine for determining whether the electronic mail message is an unwanted message.

Claims

1. A method for detecting an unwanted message, comprising:

(a) receiving an electronic mail message;

(b) decomposing text in the electronic mail message;

(c) gathering statistics associated with the text using a statistical analyzer;

(d) teaching a neural network engine coupled to the statistical analyzer to recognize unwanted messages based on statistical indicators; and

(e) analyzing the statistical indicators utilizing the neural network engine for determining whether the electronic mail message is an unwanted message.

2. The method as recited in claim 1, wherein the neural network engine is taught to recognize unwanted messages by providing examples to the neural network engine, wherein the examples are of wanted messages and unwanted messages, wherein each of the examples is associated with a desired output.

3. The method as recited in claim 2, wherein each of the examples are processed with statistics by the neural network engine for generating weights for the statistics, wherein each of the weights is used to denote wanted and unwanted messages.

4. The method as recited in claim 3, wherein the neural network engine utilizes adaptive linear combination for adjusting the weights.

5.he. The method as recited in claim 3, wherein logic associated with the neural network engine is updated based on the processing by the neural network engine.

6. The method as recited in claim 1, wherein the teaching of the neural network engine includes identifying a message as an unwanted message, the features of the message that make the message unwanted are identified, and the identified features are stored and used by the neural network to identify subsequent unwanted messages.

7. The method as recited in claim 6, wherein a graphical user interface is provided for allowing a user to identify the features of the message that make the message unwanted.

8. The method as recited in claim 1, wherein the neural network engine analyzes previous user input for determining whether the message is unwanted.

9. The method as recited in claim 1, wherein the statistics gathered using the statistical analyzer include results of an analysis of character type.

10. A computer program product for detecting an unwanted message, comprising:

(a) computer code for receiving an electronic mail message;

(b) computer code for decomposing text in the electronic mail message;

(c) computer code for gathering statistics associated with the text using a statistical analyzer;

(d) computer code for teaching a neural network engine coupled to the statistical analyzer to recognize unwanted messages based on statistical indicators; and

(e) computer code for analyzing the statistical indicators utilizing the neural network engine for determining whether the electronic mail message is an unwanted message.

11. A system for detecting an unwanted message, comprising:

(a) a statistical analyzer for gathering statistics associated with text retrieved from an electronic mail message; and

(b) a neural network engine coupled to the statistical analyzer, the neural network engine capable of being taught to recognize unwanted messages based on statistical indicators;

(c) wherein the neural network engine analyzes the statistical indicators for determining whether the electronic mail message is an unwanted message.

12. A method for teaching a neural network engine to recognize an unwanted message, comprising:

(a) providing examples to a neural network engine, wherein the examples are of wanted messages and unwanted messages, and each of the examples is associated with a desired output;

(b) processing each of the examples with statistics for generating weights for the statistics, wherein each of the weights is used to denote wanted and unwanted messages; and

(c) updating logic associated with the neural network engine based on the processing by the neural network engine.

13. The method as recited in claim 12, wherein the neural network engine utilizes adaptive linear combination for adjusting the weights.

14. The method as recited in claim 12, wherein the neural network engine is updated to recognize an unwanted message, the message is identified as an unwanted message, the features of the message that make the message unwanted are identified, and the identified features are stored and used by the neural network to identify subsequent unwanted messages.

15. The method as recited in claim 14, wherein a graphical user interface is provided for allowing a user to identify the features of the message that make the message unwanted.

16. A computer program product for teaching a neural network engine to recognize an unwanted message, comprising:

(a) computer code for providing examples to a neural network engine, wherein the examples are of wanted messages and unwanted messages, and each of the examples is associated with a desired output;

(b) computer code for processing each of the examples with statistics for generating weights for the statistics, wherein each of the weights is used to denote wanted and unwanted messages; and

(c) computer code for updating logic associated with the neural network engine based on the processing by the neural network engine.

17. A system for teaching a neural network engine to recognize an unwanted message, comprising:

(a) a neural network engine adapted to receive examples of wanted messages and unwanted messages, each of the examples being associated with a desired output, the neural network engine further adapted for processing each of the examples with statistics for generating weights for the statistics, each of the weights being used to denote wanted and unwanted messages; and

(b) wherein the neural network engine is capable of being updated.