Systems and methods for categorizing network traffic content

• US 8,782,223 B2
• Filed: 03/12/2013
• Issued: 07/15/2014
• Est. Priority Date: 06/18/2004
• Status: Active Grant

• Current Assignee: Fortinet Inc.
• Original Assignee: Fortinet Inc.
• Inventors: Xie, Michael
• Primary Examiner(s): NGUYEN, PHUOC H
• Application Number:
  US13/795,390
• Publication Number:
  US 20130262667A1
• Time in Patent Office: 490 Days
• Field of Search: 709/203, 709/217, 709/223, 709/224
• US Class Current: 709/224
• CPC Class Codes: H04L 41/00 Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
• CPC Class Codes: H04L 43/08 Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
• CPC Class Codes: H04L 51/04 Real-time or near real-time messaging, e.g. instant messaging [IM]
• CPC Class Codes: H04L 51/046 Interoperability with other network applications or services
• CPC Class Codes: H04L 51/212 using filtering or selective blocking
• CPC Class Codes: H04L 51/42 Mailbox-related aspects, e.g. synchronisation of mailboxes
• CPC Class Codes: H04L 63/0245 Filtering by information in the payload
• CPC Class Codes: H04L 63/12 Applying verification of the received information cryptographic mechanisms or cryptographic arrangements for data integrity or data verification H04L9/32
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]

Abstract

A method for categorizing network traffic content includes determining a first characterization of the network traffic content determining a first probability of accuracy associated with the first characterization, and categorizing the network traffic content based at least in part on the first characterization and the first probability of accuracy. A method for use in a process to categorize network traffic content includes obtaining a plurality of data, each of the plurality of data representing a probability of accuracy of a characterization of network traffic content, and associating each of the plurality of data with a technique for characterizing network traffic content. A method for categorizing network traffic content includes determining a characterization of the network traffic content, determining a weight value associated with the characterization, and categorizing network traffic content based at least in part on the characterization of the network traffic content and the weight value.

Claims

1. A method of categorizing network traffic content comprising:

receiving, via a network interface device, network traffic content from a network in a network traffic content categorization module;

performing, through execution of instructions on a processor, analysis on the network traffic content within the network traffic content categorization module according to at least one analysis technique to obtain at least one categorization of the network traffic content and a probability of accuracy of each of the at least one categorizations, the at least one analysis performed as a function of a database of known categorization properties of network traffic content;

storing, on a data storage device, by the network traffic content categorization module, a representation of the categorization and data derived from the network traffic content such that the stored representation of the categorization and data derived from the network traffic content is available to assist in categorizing subsequently received network traffic content.

2. The method of claim 1, further comprising:

categorizing subsequently received network traffic content according to the at least one analysis technique as a function of the stored representation of the categorization.

3. The method of claim 1, wherein storing the representation of the categorization and data derived from the network traffic content includes storing the representation in the database of known categorization properties of network traffic content.

4. The method of claim 1, wherein the network traffic content comprises text-based chat messages.

5. The method of claim 4, wherein the text-based chat messages are instant messages.

6. The method of claim 1, wherein the network traffic content comprises content received over a network according to the Hypertext Transport Protocol and an email protocol.

7. The method of claim 1, wherein performing analysis on the network traffic content includes performing at least one of an internal link analysis, an external link analysis, a meta tag analysis, and a token analysis.

8. The method of claim 1, wherein the network traffic content categorization module receives a copy of the network traffic content to build a database of categorized network traffic content and a network traffic content screening module screens network traffic content destined for users according to the database of categorized network traffic content.

9. A method of screening network traffic content comprising:

performing, through execution of instructions on a processor, analysis on received network traffic content according to at least one analysis technique to obtain at least one categorization of the network traffic content and a probability of accuracy of the at least one categorization;

determining, through execution of instructions on a processor, based on the at least one categorization of the network traffic content and the probability of accuracy of the at least one categorization, whether the network traffic content is undesirable content;

when the network traffic content is undesirable content and the probability of accuracy at least meets an accuracy threshold, preventing the undesirable content from reaching its destination; and

otherwise allowing the network traffic content to pass.

10. The method of screening network traffic content of claim 9, the method further comprising:

storing, on a data storage device, by the network traffic content categorization module, a representation of the at least one categorization, the probability of accuracy of the at least one categorization, and data derived from the network traffic content.

11. The method of claim 9, further comprising:

passing a copy of the network traffic content to a network traffic content categorization module to build a database of known network traffic content categorizations according to the at least one categorization.

12. The method of claim 9, wherein the network traffic content comprises text-based chat messages.

13. The method of claim 9, wherein the network traffic content comprises content received over a network according to a network transport protocol.

14. The method of claim 9, wherein performing analysis on the network traffic content includes performing at least one of an internal link analysis, an external link analysis, a meta tag analysis, and a token analysis.

15. A non-transitory machine-readable storage medium, with instructions thereon which when executed by a processor of a machine, causes the machine to screen network traffic content by:

performing analysis on received network traffic content according to at least one analysis technique to obtain at least one categorization of the network traffic content and a probability of accuracy of each of the at least one categorizations, the at least one analysis performed as a function of a database of known categorization properties of network traffic content;

determining based on the at least one categorization of the network traffic content and the probability of accuracy of the at least one categorization, whether the network traffic content is undesirable content;

when the network traffic content is undesirable content and the probability of accuracy at least meets an accuracy threshold, preventing the undesirable content from reaching its destination; and

otherwise allowing the network traffic content to pass.

16. The non-transitory machine-readable storage medium of claim 15, wherein the instructions when further executed, cause the machine to:

pass a copy of the network traffic content to a network traffic content categorization module to build a database of known network traffic content categorizations according to the at least one categorization.

17. The non-transitory machine-readable storage medium of claim 15, wherein the network traffic content comprises web content.

18. The non-transitory machine-readable storage medium of claim 15, wherein the web content comprises a Hypertext Markup Language page.

19. The non-transitory machine-readable storage medium of claim 15, wherein performing analysis on the network traffic content includes performing at least one of an internal link analysis, an external link analysis, a meta tag analysis, and a token analysis.