Transcript Honeypot, DDoS, and Rootkit

Introduction to Honeypot, Denial-ofService, and Rootkit
Cliff C. Zou
CAP6135
Spring, 2010
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What Is a Honeypot?
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Abstract definition:
“A honeypot is an information
system resource whose value lies
in unauthorized or illicit use of
that resource.” (Lance Spitzner)
Concrete definition:
“A honeypot is a faked
vulnerable system used for the
purpose of being attacked,
probed, exploited and
compromised.”
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Example of a Simple Honeypot
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Install vulnerable OS and software on a
machine
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Install monitor or IDS software
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Connect to the Internet (with global IP)
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Wait & monitor being scanned, attacked,
compromised
Finish analysis, clean the machine
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Benefit of Deploying Honeypots
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Risk mitigation:
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Lure an attacker away from the real production
systems (“easy target“).
IDS-like functionality:
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Since no legitimate traffic should take place to or
from the honeypot, any traffic appearing is evil
and can initiate further actions.
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Benefit of Deploying Honeypots
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Attack analysis:
Find out reasons, and strategies why and how
you are attacked.
 Binary and behavior analysis of capture
malicious code
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Evidence:
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Once the attacker is identified, all data captured
may be used in a legal procedure.
Increased knowledge
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Honeypot Classification
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High-interaction honeypots
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A full and working OS is provided for being attacked
VMware virtual environment
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Low-interaction honeypots
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Only emulate specific network services
No real interaction or OS
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Several VMware virtual hosts in one physical machine
Honeyd
Honeynet/honeyfarm
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A network of honeypots
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Low-Interaction Honeypots
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Pros:
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Easy to install (simple program)
No risk (no vulnerable software to be attacked)
One machine supports hundreds of honeypots, covers
hundreds of IP addresses
Cons:
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No real interaction to be captured
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Limited logging/monitor function
Hard to detect unknown attacks; hard to generate filters
Easily detectable by attackers
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High-Interaction Honeypots
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Pros:
Real OS, capture all attack traffic/actions
 Can discover unknown attacks/vulnerabilites
 Can capture and anlayze code behavior
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Cons:
Time-consuming to build/maintain
 Time-consuming to analysis attack
 Risk of being used as stepping stone
 High computer resource requirement
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Honeynet
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A network of honeypots
High-interaction honeynet
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Low-interaction honeynet
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A distributed network composing many honeypots
Emulate a virtual network in one physical machine
Example: honeyd
Mixed honeynet
 “Scalability, Fidelity and Containment in the
Potemkin Virtual Honeyfarm”, presented next
week
Reference: http://www.ccc.de/congress/2004/fahrplan/files/135honeypot-forensics-slides.ppt
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Honeypot-Aware Botnet [Zou’07]
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Honeypot is widely used by defenders
Ability to detect unknown attacks
 Ability to monitor attacker actions (e.g., botnet
C&C)
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Botnet attackers will adapt to honeypot
defense
When they feel the real threat from honeypot
 We need to think one step ahead
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Honeypot Detection Principles
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Hardware/software specific honeypot detection
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Detect virtual environment via specific code
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E.g., time response, memory address
Detect faculty honeypot program
Case by case detection
Detection based on fundamental difference
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Honeypot defenders are liable for attacks sending out
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Liability law will become mature
It’s a moral issue as well
Real attackers bear no liability
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Check whether a bot can send out malicious traffic or not
Detection of Honeypot Bot
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bot
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1 malicious traffic
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Sensor (secret)
C&C
Infection traffic
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Real liability to defenders
No exposure issue: a bot needs to do this regardless
Other honeypot detection traffic
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Port scanning, email spam, web request (DoS?)
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Two-stage Reconnaissance to Detect
Honeypot in Constructing P2P Botnets
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Host A
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3
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spearhead
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Host B
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2
spearhead
request
main-force
Fully distributed
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No central sensor is used
Could be fooled by double-honeypot
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1
Counterattack is presented in our paper
Lightweighted spearhead code
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Infect + honeypot detection
Speedup UDP-based infection
Host C
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Defense against
Honeypot-Aware Attacks
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Permit dedicated honeypot detection systems to
send out malicious traffic
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Redirect outgoing traffic to a second honeypot
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Not effective for sensor-based honeypot detection
Figure out what outgoing traffic is for honeypot
detection, and then allow it
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Need law and strict policy
It could be very hard
Neverthless, honeypot is still a valuable monitoring
and
detection/defense tool
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Distributed Denial of Service
(DDoS) Attack
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Send large amount of traffic to a server so that the
server has no resource to serve normal users
Attacking format:
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Consume target memory/CPU resource
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SYN flood (backscatter paper presented before)
Database query…
Congest target Internet connection
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Many sources attack traffic overwhelm target link
Very hard to defend
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Why hard to defined DDoS attack?
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Internet IP protocol has no built-in security
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No authentication of source IP
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SYN flood with faked source IP
However, IP is true after connection is setup
Servers are supposed to accept unsolicited service
requests
Lack of collaboration ways among Internet
community
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How can you ask an ISP in another country to block
certain traffic for you?
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DDoS Defenses
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Increase servers capacity
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Use Internet web caching service
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Cluster of machine, Multi-CPUs, larger Internet
access
E.g., Akamai
Defense Methods (many in research stage)
SYN cookies (http://en.wikipedia.org/wiki/SYN_cookies)
 SOS
 IP traceback
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SYN Cookies
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SYN flood attack
Fill up server’s SYN queue
 Property: attacker does not respond to SYN/ACK
from victim.
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Defense
Fact: normal client responds to SYN/ACK
 Remove initial SYN queue
 Server encode info in TCP seq. number
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Use it to reconstruct the initial SYN
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DoS spoofed attack defense: IP
traceback
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Suppose a victim can call ISPs upstream to
block certain traffic
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SYN flood: which traffic to block?
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IP traceback:
Find out the real attacking host for SYN flood
 Based on large amount of attacking packets
 Need a little help from routers (packet marking)
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SOS: Secure Overlay Service
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Central Idea:
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Use many TCP connection respondent machines
Only setup connections relay to server
 Identity of server is secrete
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The Evolution of Malware
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Malware, including spyware, adware and viruses
want to be hard to detect and/or hard to remove
Rootkits are a fast evolving technology to achieve
these goals
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Cloaking technology applied to malware
Not malware by itself
Example rootkit-based viruses: W32.Maslan.A@mm,
W32.Opasa@mm
Rootkit history
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Appeared as stealth viruses
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One of the first known PC viruses, Brain, was stealth
First “rootkit” appeared on SunOS in 1994
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Replacement of core system utilities (ls, ps, etc.) to hide malware
processes
Cloaking
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Modern rootkits can cloak:
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Several major rootkit technologies
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Processes
Services
TCP/IP ports
Files
Registry keys
User accounts
User-mode API filtering
Kernel-mode API filtering
Kernel-mode data structure manipulation
Process hijacking
Visit www.rootkit.com for tools and information
User-Mode API Filtering
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Attack user-mode system query APIs
Taskmgr.exe
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Explorer.exe,
Winlogon.exe
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Ntdll.dll
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Rootkit
user mode
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kernel mode
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Explorer.exe, Malware.exe, Winlogon.e
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Con: can be bypassed by going directly to kernelmode APIs
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Pro: can infect unprivileged user accounts
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Examples: HackerDefender, Afx
Kernel-Mode API Filtering
Attack kernel-mode system query APIs
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Taskmgr.exe
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Explorer.exe,
Winlogon.exe
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Ntdll.dll
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user mode
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kernel mode
Explorer.exe,
Winlogon.exe
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Explorer.exe, Malware.exe,
Winlogon.exe
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Cons:
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Requires admin privilege to install
Difficult to write
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Pro: very thorough cloak
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Example: NT Rootkit
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Rootkit
Kernel-Mode Data Structure
Manipulation
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Also called Direct Kernel Object Manipulation
Attacks active process data structure
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Query API doesn’t see the process
Kernel still schedules process’ threads
Active Explorer.exe
Processes
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Cons:
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Malware.exe
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Requires admin privilege to install
Can cause crashes
Detection already developed
Pro: more advanced variations possible
Example: FU
Winlogon.exe
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Process Hijacking
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Hide inside a legitimate process
Explorer.exe
Malware
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Con: doesn’t survive reboot
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Pro: extremely hard to detect
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Example: Code Red
Detecting Rootkits
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All cloaks have holes
Leave some APIs unfiltered
 Have detectable side effects
 Can’t cloak when OS is offline
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Rootkit detection attacks holes
Cat-and-mouse game
 Several examples
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Microsoft Research Strider/Ghostbuster
RKDetect
Sysinternals RootkitRevealer
F-Secure BlackLight
Simple Rootkit Detection
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Perform a directory listing online and
compare with secure alternate OS boot
(see http://research.microsoft.com/rootkit/ )
Offline OS is Windows PE, ERD
Commander, BartPE
dir /s /ah * > dirscan.txt
windiff dirscanon.txt
dirscanoff.txt
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This won’t detect non-persistent rootkits
that save to disk during shutdown
RootkitRevealer
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RootkitRevealer (RKR) runs online
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RKR tries to bypass rootkit to uncover cloaked objects
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All detectors listed do the same
RKR scans HKLM\Software, HKLM\System and the file
system
Performs Windows API scan and compares with raw data
structure scan
RootkitRevealer
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Filtered Windows API
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Rootkit
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omits malware files and keys
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Malware files and keys
are visible in raw scan
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Windows API
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Raw file system,
Raw Registry hive
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Demo
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HackerDefender
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HackerDefender before and after view of file system
Detecting HackerDefender with RootkitRevealer
RootkitRevealer Limitations
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Rootkits have already attacked RKR
directly by not cloaking when scanned
RKR is given true system view
 Windows API scan looks like raw scan
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SysInternals have modified RKR to be a
harder to detect by rootkits
RKR is adopting rootkit techniques itself
 Rootkit authors will continue to find ways
around RKR’s cloak
 It’s a game nobody can win
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Dealing with Rootkits
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Unless you have specific uninstall
instructions from an authoritative source:
Reformat the system and reinstall Windows!
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Don’t rely on “rename” functionality
offered by some rootkit detectors
It might not have detected all a rootkit’s
components
 The rename might not be effective
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