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

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

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)

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

Botnet Life-cycle

Botnet Life-cycle

Botnet Life-cycle

Botnet Life-cycle

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

Botnet is Used For Bot Master

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

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

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

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

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

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

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

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

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

Botnet Research: Monitoring Attacker Infected Hi! IRC Herder Researcher

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

References B. Saha and A, Gairola, “Botnet: An overview,” CERT-In White PaperCIWP-2005-05, 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, 2009. 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, 2009. NCA 2009. Eighth IEEE International Symposium. Publication Year: 2009 , Page(s): 156 – 162 IEEE CONFERENCES Spamming botnets: signatures and characteristics Yinglian Xie, Fang Yu

QUESTIONS

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