1. BotNet Detection Techniques By Shreyas Sali
Course: Network Security (CSCI – 5235)
Instructor: Dr. T Andrew Yang 1

2. Outline Introduction to Botnet Botnet Life-cycle
Botnet in Network Security
Botnet Uses
Botnet Detection
Preventing Botnet Infection
Botnet Research
Conclusion
References

3. Introduction to Botnet
A Botnet is a network of compromised computers under the control of a remote attacker.
Botnet Terminology
Bot Herder (Bot Master)
Bot
Bot Client
IRC Server
Command and Control Channel (C&C)

4. Introduction to Botnet (Terminology)
IRC Server
IRC Channel
Code Server
Bot Master
IRC Channel
C&C Traffic
Updates
Attack
Victim
Bots

5. Botnet Life-cycle

6. Botnet Life-cycle

7. Botnet Life-cycle

8. Botnet Life-cycle

9. Botnet In Network Security
Internet users are getting infected by bots
Many times corporate and end users are trapped in botnet attacks
Today 16-25% of the computers connected to the internet are members of a botnet
In this network bots are located in various locations
It will become difficult to track illegal activities
This behavior makes botnet an attractive tool for intruders and increase threat against network security

10. Botnet is Used For
Bot Master

11. So It is really Important to Detect this attack
How Botnet is Used?
Distributed Denial of Service (DDoS) attacks
Sending Spams
Phishing (fake websites)
Addware (Trojan horse)
Spyware (keylogging, information harvesting)
Click Fraud
So It is really Important to Detect this attack

12. Botnet Detection Two approaches for botnet detection based on
Setting up honeynets
Passive traffic monitoring
Signature based
Anomaly based
DNS based
Mining based

13. Botnet Detection: Setting up Honeynets
Windows Honeypot
Honeywall Responsibilities:
DNS/IP-address of IRC server and port number
(optional) password to connect to IRC-server
Nickname of bot
Channel to join and (optional) channel-password

14. Botnet Detection: Setting up Honeynets
Sensor
1. Malicious Traffic
3. Authorize
2. Inform bot’s IP
Bot Master

15. Botnet Detection: Traffic Monitoring
Signature based: Detection of known botnets
Anomaly based: Detect botnet using following anomalies
High network latency
High volume of traffic
Traffic on unusual port
Unusual system behaviour
DNS based: Analysis of DNS traffic generated by botnets

16. Botnet Detection: Traffic Monitoring
Mining based:
Botnet C&C traffic is difficult to detect
Anomaly based techniques are not useful
Data Mining techniques – Classification, Clustering

17. Botnet Detection
Determining the source of a botnet-based attack is challenging:
Traditional approach:
Every zombie host is an attacker
Botnets can exist in a benign state for an arbitrary amount of time before they are used for a specific attack
New trend:
P2P networks

18. Preventing Botnet Infections
Use a Firewall
Patch regularly and promptly
Use Antivirus (AV) software
Deploy an Intrusion Prevention System (IPS)
Implement application-level content filtering
Define a Security Policy and
Share Policies with your users systematically

19. Botnet Research Logging onto herder IRC server to get info
Passive monitoring
Either listening between infected machine and herder or spoofing infected PC
Active monitoring: Poking around in the IRC server
Sniffing traffic between bot & control channel

20. Botnet Research: Monitoring Attacker
Infected
Hi!
IRC
Herder
Researcher

21. Conclusion
Botnets pose a significant and growing threat against cyber security
It provides key platform for many cyber crimes (DDOS)
As network security has become integral part of our life and botnets have become the most serious threat to it
It is very important to detect botnet attack and find the solution for it

22. References
B. Saha and A, Gairola, “Botnet: An overview,” CERT-In White PaperCIWP , 2005
Peer to Peer Botnet detection for cyber-security: A data mining approach - ACM Portal Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, Bhavani Thuraisingham
A Survey of Botnet and Botnet Detection Feily, M.; Shahrestani, A.; Ramadass, S.;  Emerging Security Information, Systems and Technologies, SECURWARE '09. Third International Conference on Digital Object Publication Year: 2009 , Page(s): 268 – 273 IEEE CONFERENCES
Honeynet-based Botnet Scan Traffic Analysis Zhichun Li, Anup Goyal, and Yan Chen Northwestern University, Evanston, IL 60208
Detecting Botnets Using Command and Control Traffic AsSadhan, B.; Moura, J.M.F.; Lapsley, D.; Jones, C.; Strayer, W.T.; Network Computing and Applications, NCA Eighth IEEE International Symposium. Publication Year: 2009 , Page(s): 156 – 162 IEEE CONFERENCES
Spamming botnets: signatures and characteristics Yinglian Xie, Fang Yu

23. QUESTIONS

24. Thank you