a multifaceted approach to understanding the botnet phenomenon

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A Multifaceted Approach to Understanding the Botnet Phenomenon Authors : Moheeb Abu Rajab, Jay Zarfoss, Fabian Monrose, Andreas Terzis Computer Science Department Johns Hopkins University Presented at : Internet Measurement Conference, IMC'06, Brazil, October 2006 Presented By :

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A Multifaceted Approach to Understanding the Botnet Phenomenon. Authors : Moheeb Abu Rajab, Jay Zarfoss, Fabian Monrose, Andreas Terzis Computer Science Department Johns Hopkins University Presented at : Internet Measurement Conference, IMC'06, Brazil, October 2006 Presented By : - PowerPoint PPT Presentation

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Page 1: A Multifaceted Approach to Understanding the Botnet Phenomenon

A Multifaceted Approach to Understanding the BotnetPhenomenon

Authors :Moheeb Abu Rajab, Jay Zarfoss, Fabian Monrose, Andreas TerzisComputer Science DepartmentJohns Hopkins UniversityPresented at : Internet Measurement Conference, IMC'06, Brazil, October 2006Presented By :Ramanarayanan Ramani

Page 2: A Multifaceted Approach to Understanding the Botnet Phenomenon

Outline Working of Botnets Measuring Botnets Inference from Measurement Strengths Weaknesses Suggestions

Page 3: A Multifaceted Approach to Understanding the Botnet Phenomenon

Botnets A botnet is a network of infected end-hosts

(bots) under the command of a botmaster.

3 Different Protocols Used: IRC HTTP P2P

Page 4: A Multifaceted Approach to Understanding the Botnet Phenomenon

Botnets (contd.)3 Steps of Authentication

Bot to IRC Server

IRC Server to Bot

Botmaster to Bot

(*) : Optional Step

Page 5: A Multifaceted Approach to Understanding the Botnet Phenomenon

Measuring Botnets Three Distinct Phases

Malware CollectionCollect as many bot binaries as possible

Binary analysis via gray-box testingExtract the features of suspicious binaries

Longitudinal trackingTrack how bots spread and its reach

Page 6: A Multifaceted Approach to Understanding the Botnet Phenomenon

Measuring Botnets

Darknet : Denotes an allocated but unused portion of the IP address space.

Page 7: A Multifaceted Approach to Understanding the Botnet Phenomenon

Malware Collection Nepenthes is a low interaction honeypot Nepenthes mimics the replies generated by

vulnerable services in order to collect the first stage exploit

Modules in nepenthes Resolve DNS asynchronous Emulate vulnerabilities Download files – Done here by the Download Station Submit the downloaded files Trigger events Shellcode handler

Page 8: A Multifaceted Approach to Understanding the Botnet Phenomenon

Malware Collection Honeynets also used along

with nepenthes Catches exploits missed by nepenthes Unpatched Windows XP are run which is

base copy Infected honeypot compared with base to

identify Botnet binary

Page 9: A Multifaceted Approach to Understanding the Botnet Phenomenon

Gateway Routing to different components Firewall : Prevent outbound attacks & self

infection by honeypots Detect & Analyze outgoing traffic for

infections in honeypot Only 1 infection in a honeypot Several other functions

Page 10: A Multifaceted Approach to Understanding the Botnet Phenomenon

Binary Analysis Two logically distinct phases

Derive a network fingerprint of the binary

Derive IRC-specific features of the binary

IRC Server learns Botnet “dialect” - Template Learn how to correctly mimic bot’s behavior -

Subject bot to a barrage of commands

Page 11: A Multifaceted Approach to Understanding the Botnet Phenomenon

IRC Tracker Use template to mimic bot Connect to real IRC server Communicate with botmaster using bot

“dialect” Drones modified and used to act as IRC

Client by the tracker to Cover lot of IP addresss

Page 12: A Multifaceted Approach to Understanding the Botnet Phenomenon

DNS Tracker Bots issue DNS queries to resolve the IP

addresses of their IRC servers Tracker uses DNS requests Has 800,000 entries after reduction Maintain hits to a server

Page 13: A Multifaceted Approach to Understanding the Botnet Phenomenon

Measuring Botnets

Darknet : Denotes an allocated but unused portion of the IP address space.

Page 14: A Multifaceted Approach to Understanding the Botnet Phenomenon

Botnet Traffic Share

Page 15: A Multifaceted Approach to Understanding the Botnet Phenomenon

Botnet Traffic Share

Page 16: A Multifaceted Approach to Understanding the Botnet Phenomenon

DNS Tracker Results

Page 17: A Multifaceted Approach to Understanding the Botnet Phenomenon

Bot Scan Method 2 Types

Immediately start scanning the IP space looking for new victims after infection : 34 / 192

Scan when issued some command by botmaster

Page 18: A Multifaceted Approach to Understanding the Botnet Phenomenon

Botnet Growth - DNS

Page 19: A Multifaceted Approach to Understanding the Botnet Phenomenon

Botnet Growth – IRC Tracker

Page 20: A Multifaceted Approach to Understanding the Botnet Phenomenon

Botnet Online Population

Page 21: A Multifaceted Approach to Understanding the Botnet Phenomenon

Botnet Online Population

Page 22: A Multifaceted Approach to Understanding the Botnet Phenomenon

Botnet Software TaxonomyServices Launched in Victim Machine OS of Exploited Host

Page 23: A Multifaceted Approach to Understanding the Botnet Phenomenon

Botmaster Analysis

Page 24: A Multifaceted Approach to Understanding the Botnet Phenomenon

Strengths All aspects of a botnet analyzed No prior analysis of bots Ability to model various types of bots

Page 25: A Multifaceted Approach to Understanding the Botnet Phenomenon

Weakness Only Microsoft Windows systems

analyzed Focus on IRC-based bots as they are

predominant

Page 26: A Multifaceted Approach to Understanding the Botnet Phenomenon

Suggestions Use the analysis to model new bots Use the analysis to model protection

methods

Page 27: A Multifaceted Approach to Understanding the Botnet Phenomenon

Questions