SYSTEM AND METHOD FOR MODELING ACTIVITY PATTERNS OF NETWORK TRAFFIC TO DETECT BOTNETS

• US 20110153811A1
• Filed: 06/23/2010
• Published: 06/23/2011
• Est. Priority Date: 12/18/2009
• Status: Abandoned Application

• Current Assignee: Korea Internet & Security Agency
• Original Assignee: Korea Internet & Security Agency
• Inventors: Lee, Tae Jin, Won, Yong Geun, Ji, Seung Gao, Oh, Joo Hyung, Kang, Dong Wan, Jeong, Hyun Cheol, IM, Chae Tae
• Application Number:
  US12/821,510
• Publication Number:
  US 20110153811A1
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: H04L 2463/144 Detection or countermeasures against botnets
• CPC Class Codes: H04L 63/14 for detecting or protecting against malicious traffic

Abstract

The invention relates to a system and method that can detect botnets by classifying the communication activities for each client according to destination or based on similarity between the groups of collected traffic. According to certain aspects of the invention, the communication activities for each client can be classified to model network activity by differentiating the protocols of the collected network traffic based on destination and patterning the subgroups for the respective protocols. Those servers that are estimated to be C&C servers can be classified into download and upload, spam servers and command control servers, within a botnet group detected by modeling network activity, i.e. analyzing network-based activity patterns. Also, botnet groups can be detected by way of a group information management function, for generating an activity pattern-based group matrix based on group data, and a mutual similarity analysis, performed on groups suspected to be botnets from the group information.

Claims

1. A system for modeling activity patterns of network traffic to detect botnets, the system comprising:

a botnet traffic collector sensor configured to collect traffic within a network and classify the traffic according to destination; and

a botnet detector system configured to detect a botnet based on botnet traffic collected by the botnet traffic collector sensor.

2. The system of claim 1, wherein the botnet detector system arranges the traffic classified according to destination into groups for different time periods and then detects a botnet group having a particular access pattern exceeding a threshold number.

3. The system of claim 1, wherein the botnet traffic collector sensor comprises:

a traffic information collector module configured to collect traffic by capturing packets of a monitored network according to a collecting policy using a packet capturing tool;

a traffic information manager module configured to classify information received from the traffic information collector module, receive and parse traffic information, process group data, and store/manage the traffic information in a database;

a traffic information transmitter module configured to differentiate the traffic information parsed at the traffic information manager module into a transmission header and transmission data, package the data, and transmit the data by way of a transmission channel; and

a sensor policy manager module configured to transmit settings/status information of a classification tool, a traffic information manager tool, and data transmission cycle information to the traffic information collector module, the traffic information manager module, and the traffic information transmitter module.

4. The system of claim 3, wherein the traffic information manager module classifies patterns of the collected network traffic into transmission control protocols (TCP) and user datagram protocols (UDP).

5. The system of claim 4, wherein the traffic information manager module classifies the transmission control protocols (TCP) into hypertext transport protocols (HTTP), simple mail transfer protocols (SMTP), and other transmission control protocols besides the hypertext transport protocols and the simple mail transfer protocols,

and classifies the hypertext transport protocols into “

requests”

for pages and “

responses”

from servers to user requests.

6. The system of claim 5, wherein a simple mail transfer protocol communication is used as pattern data for the simple mail transfer protocols (SMTP),

and a user datagram protocol communication is determined as pattern data for the user data protocols (UDP).

7. The system of claim 5, wherein the “

request”

is classified into a host portion, which is a domain of a target of a request for a web server resource, a page portion, which includes information on a particular page desired by the host, and a referrer portion, which includes information on steps preceding a website currently accessed.

8. The system of claim 4, wherein the traffic information manager module classifies the user datagram protocols (UDP) into a domain name server (DNS) and other user datagram protocols besides the domain name server.

9. A method for modeling activity patterns of network traffic to detect botnets, the method comprising:

collecting traffic;

classifying protocols of the collected traffic; and

modeling activities for the classified traffic.

10. The method of claim 9, wherein the classifying of the collected traffic comprises:

arranging the collected traffic into client sets according to destination; and

extracting feature elements of the traffic arranged into client sets according to destination.

11. The method of claim 10, wherein the arranging of the collected traffic into client sets according to destination comprises:

storing access records of the collected traffic; and

arranging the collected traffic into client sets according to destination.

12. A method for modeling activity patterns of network traffic to detect botnets, the method comprising:

collecting traffic;

generating group information for the collected traffic; and

determining a botnet group based on the group information,

wherein the group information includes group data and a group matrix, the group data including information on a plurality of sources for a single destination, the group matrix including stored data obtained after analyzing an IP count according to an access activity pattern occurring in the group data.

13. The method of claim 12, wherein the generating of the group information for the collected traffic comprises:

classifying the collected traffic according to protocol.

14. The method of claim 13, wherein the classifying of the collected traffic according to protocol comprises:

arranging the collected traffic into client sets according to destination.

15. The method of claim 12, wherein the determining of the botnet group based on the group information comprises:

managing group matrices; and

if a particular access pattern exceeds a threshold number for each of the group matrices, selecting the corresponding group as an analysis target group.

16. The method of claim 15, wherein the managing of the group matrices comprises:

generating a group matrix if the group matrix does not exist;

updating a group matrix if the group matrix does exist; and

deleting a group matrix if the group matrix has not been updated for a particular duration or by a particular proportion.

17. The method of claim 12, further comprising:

analyzing client similarity with respect to a particular access pattern for the group matrices selected as analysis targets.

18. The method of claim 17, wherein the analyzing of client similarity comprises:

among the group matrices selected as analysis targets, if the client similarity with respect to a particular access pattern for the group matrices is greater than a particular value for the group matrices of which the similarity is compared, then determining that the group matrices of which the similarity is compared belong to a same botnet group.