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Botnet Detection by Monitoring Group Activities in DNS Traffic

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Presentation on theme: "Botnet Detection by Monitoring Group Activities in DNS Traffic"— Presentation transcript:

1 Botnet Detection by Monitoring Group Activities in DNS Traffic
Speaker: Jun-Yi Zheng 2009/11/23

2 Reference H. Choi, H. Lee, H. Lee, and H. Kim. Botnet detection by monitoring group activities in dns traffic. In Proceedings of the 7th IEEE International Conference on Computer and Information Technology (CIT’07), Washington, DC, October 2007.

3 Outline INTRODUCTION FEATURES of BOTNET DNS
DNS-BASED BOTNET DETECTION MECHANISM EVALUATION CONCLUSION

4 Introduction Most of bots use DNS in rallying process

5 Rally Problem Static IP address or DDNS?

6 C&C Server Migration Botnets were migrate their C&C server frequently
There observed most of them (65%) are moved only up for 1 day

7 Features of Botnet DNS At the rallying procedure
At the malicious behaviors of a botnet At C&C server link failures At C&C server migration At C&C server IP address changes

8 Differences Source IPs accessed to domain name
Activity and Appearance Patterns DNS Type Botnet DNS Fixed size Group (Botnet members) Group activity Intermittently appeared (Specific situation) Usually DDNS Legitimate Anonymous (Legitimate users) Non-group activity Randomly and continuously appered (Usually)

9 Botnet DNS Query Detection Algorithm
Insert-DNS-Query

10 Botnet DNS Query Detection Algorithm
Delete-DNS-Query If the size of IP list do not exceed the size threshold or the domain name is legitimate which already exist in a whitelist Detect-BotDNS-Query Similarity A C B

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12 Migrating Botnet Detection Algorithm
Insert-DNS-Query Delete-DNS-Query Detect-BotDNS-Query compare the IP lists of different domain name which have similar size of IP list

13 Evaluation the system is executed on a campus network with botnet
50 machines are used in the botnet (Agobot) captured the traffic for 10 hours parameter A time unit is 1 hour A size threshold for the detection algorithm is 5(size of IP List) similarity threshold is 0.8

14 Botnet DNS Query Detection
During 1 hour Over 80% was 1 92.5% 5

15 Botnet DNS Query Detection
(a),(c),(d),(e) were identified as P2P cites or a cite of enormous size of file transferring

16 Migrating Botnet Detection
the ”similar size” are settled within 10% of the size of IP list

17 Conclusions significant features of botnet DNS queries
a simple mechanism to detect a botnet by using a DNS queries The two different algorithm for botnet detection


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