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Introduction to Honeypot, Denial-of-Service, and Rootkit. Cliff C. Zou CAP6135 Spring, 2010. What Is a Honeypot?. Abstract definition: “A honeypot is an information system resource whose value lies in unauthorized or illicit use of that resource.” (Lance Spitzner) Concrete definition:

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what is a honeypot
What Is a Honeypot?
  • 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.”

example of a simple honeypot
Example of a Simple Honeypot
  • Install vulnerable OS and software on a machine
  • Install monitor or IDS software
  • Connect to the Internet (with global IP)
  • Wait & monitor being scanned, attacked, compromised
  • Finish analysis, clean the machine
benefit of deploying honeypots
Benefit of Deploying Honeypots
  • Risk mitigation:
    • Lure an attacker away from the real production systems (“easy target“).
  • IDS-like functionality:
    • Since no legitimate traffic should take place to or from the honeypot, any traffic appearing is evil and can initiate further actions.
benefit of deploying honeypots1
Benefit of Deploying Honeypots
  • Attack analysis:
    • Find out reasons, and strategies why and how you are attacked.
    • Binary and behavior analysis of capture malicious code
  • Evidence:
    • Once the attacker is identified, all data captured may be used in a legal procedure.
  • Increased knowledge
honeypot classification
Honeypot Classification
  • High-interaction honeypots
    • A full and working OS is provided for being attacked
    • VMware virtual environment
      • Several VMware virtual hosts in one physical machine
  • Low-interaction honeypots
    • Only emulate specific network services
    • No real interaction or OS
      • Honeyd
  • Honeynet/honeyfarm
    • A network of honeypots
low interaction honeypots
Low-Interaction Honeypots
  • Pros:
    • 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:
    • No real interaction to be captured
      • Limited logging/monitor function
      • Hard to detect unknown attacks; hard to generate filters
    • Easily detectable by attackers
high interaction honeypots
High-Interaction Honeypots
  • Pros:
    • Real OS, capture all attack traffic/actions
    • Can discover unknown attacks/vulnerabilites
    • Can capture and anlayze code behavior
  • Cons:
    • Time-consuming to build/maintain
    • Time-consuming to analysis attack
    • Risk of being used as stepping stone
    • High computer resource requirement
  • A network of honeypots
  • High-interaction honeynet
    • A distributed network composing many honeypots
  • Low-interaction honeynet
    • 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:
honeypot aware botnet zou 07
Honeypot-Aware Botnet [Zou’07]
  • Honeypot is widely used by defenders
    • Ability to detect unknown attacks
    • Ability to monitor attacker actions (e.g., botnet C&C)
  • Botnet attackers will adapt to honeypot defense
    • When they feel the real threat from honeypot
    • We need to think one step ahead
honeypot detection principles
Honeypot Detection Principles
  • Hardware/software specific honeypot detection
    • Detect virtual environment via specific code
      • E.g., time response, memory address
    • Detect faculty honeypot program
    • Case by case detection
  • Detection based on fundamental difference
    • Honeypot defenders are liable for attacks sending out
      • Liability law will become mature
      • It’s a moral issue as well
    • Real attackers bear no liability
      • Check whether a bot can send out malicious traffic or not
detection of honeypot bot
Detection of Honeypot Bot
  • Infection traffic
    • Real liability to defenders
    • No exposure issue: a bot needs to do this regardless
  • Other honeypot detection traffic
    • Port scanning, email spam, web request (DoS?)
  • bot
  • Sensor (secret)
  • 1 malicious traffic
  • 2 Inform bot’s IP
  • 3 Authorize
  • C&C
two stage reconnaissance to detect honeypot in constructing p2p botnets
Two-stage Reconnaissance to Detect Honeypot in Constructing P2P Botnets
  • Fully distributed
    • No central sensor is used
    • Could be fooled by double-honeypot
      • Counterattack is presented in our paper
  • Lightweighted spearhead code
    • Infect + honeypot detection
    • Speedup UDP-based infection
  • 1
  • Host A
  • Host B
  • Host C
  • 2
  • spearhead
  • spearhead
  • request
  • main-force
  • 3
defense against honeypot aware attacks
Defense against Honeypot-Aware Attacks
  • Permit dedicated honeypot detection systems to send out malicious traffic
    • Need law and strict policy
  • Redirect outgoing traffic to a second honeypot
    • Not effective for sensor-based honeypot detection
  • Figure out what outgoing traffic is for honeypot detection, and then allow it
    • It could be very hard
  • Neverthless, honeypot is still a valuable monitoring and detection/defense tool
distributed denial of service ddos attack
Distributed Denial of Service (DDoS) Attack
  • Send large amount of traffic to a server so that the server has no resource to serve normal users
  • Attacking format:
    • Consume target memory/CPU resource
      • SYN flood (backscatter paper presented before)
      • Database query…
    • Congest target Internet connection
      • Many sources attack traffic overwhelm target link
      • Very hard to defend
why hard to defined ddos attack
Why hard to defined DDoS attack?
  • Internet IP protocol has no built-in security
    • No authentication of source IP
      • 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
    • How can you ask an ISP in another country to block certain traffic for you?
ddos defenses
DDoS Defenses
  • Increase servers capacity
    • Cluster of machine, Multi-CPUs, larger Internet access
  • Use Internet web caching service
    • E.g., Akamai
  • Defense Methods (many in research stage)
    • SYN cookies (
    • SOS
    • IP traceback
syn cookies
SYN Cookies
  • SYN flood attack
    • Fill up server’s SYN queue
    • Property: attacker does not respond to SYN/ACK from victim.
  • Defense
    • Fact: normal client responds to SYN/ACK
    • Remove initial SYN queue
    • Server encode info in TCP seq. number
      • Use it to reconstruct the initial SYN
dos spoofed attack defense ip traceback
DoS spoofed attack defense: IP traceback
  • Suppose a victim can call ISPs upstream to block certain traffic
  • SYN flood: which traffic to block?
  • 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)
sos secure overlay service
SOS: Secure Overlay Service
  • Central Idea:
    • Use many TCP connection respondent machines
    • Only setup connections relay to server
    • Identity of server is secrete
the evolution of malware
The Evolution of Malware
  • 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
    • Cloaking technology applied to malware
    • Not malware by itself
    • Example rootkit-based viruses: W32.Maslan.A@mm, W32.Opasa@mm
  • Rootkit history
    • Appeared as stealth viruses
      • One of the first known PC viruses, Brain, was stealth
    • First “rootkit” appeared on SunOS in 1994
      • Replacement of core system utilities (ls, ps, etc.) to hide malware processes
  • Modern rootkits can cloak:
    • Processes
    • Services
    • TCP/IP ports
    • Files
    • Registry keys
    • User accounts
  • Several major rootkit technologies
    • User-mode API filtering
    • Kernel-mode API filtering
    • Kernel-mode data structure manipulation
    • Process hijacking
  • Visit for tools and information
user mode api filtering


Explorer.exe, Malware.exe, Winlogon.exe

User-Mode API Filtering
  • Attack user-mode system query APIs
  • Con: can be bypassed by going directly to kernel-mode APIs
  • Pro: can infect unprivileged user accounts
  • Examples: HackerDefender, Afx




user mode

kernel mode

kernel mode api filtering



Explorer.exe, Malware.exe,Winlogon.exe

Kernel-Mode API Filtering



  • Attack kernel-mode system query APIs
  • Cons:
    • Requires admin privilege to install
    • Difficult to write
  • Pro: very thorough cloak
  • Example: NT Rootkit

user mode

kernel mode


kernel mode data structure manipulation
Kernel-Mode Data Structure Manipulation
  • Also called Direct Kernel Object Manipulation
  • Attacks active process data structure
    • Query API doesn’t see the process
    • Kernel still schedules process’ threads
  • Cons:
    • Requires admin privilege to install
    • Can cause crashes
    • Detection already developed
  • Pro: more advanced variations possible
  • Example: FU





process hijacking
Process Hijacking
  • Hide inside a legitimate process
  • Con: doesn’t survive reboot
  • Pro: extremely hard to detect
  • Example: Code Red



detecting rootkits
Detecting Rootkits
  • All cloaks have holes
    • Leave some APIs unfiltered
    • Have detectable side effects
    • Can’t cloak when OS is offline
  • Rootkit detection attacks holes
    • Cat-and-mouse game
    • Several examples
      • Microsoft Research Strider/Ghostbuster
      • RKDetect
      • Sysinternals RootkitRevealer
      • F-Secure BlackLight
simple rootkit detection
Simple Rootkit Detection
  • Perform a directory listing online and compare with secure alternate OS boot (see )
    • Offline OS is Windows PE, ERD Commander, BartPE

dir /s /ah * > dirscan.txt

windiff dirscanon.txt dirscanoff.txt

  • This won’t detect non-persistent rootkits that save to disk during shutdown

Filtered Windows API

omits malware files and keys

Malware files and keys are visible in raw scan

  • RootkitRevealer (RKR) runs online
  • RKR tries to bypass rootkit to uncover cloaked objects
    • 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



Windows API

Raw file system, Raw Registry hive

  • HackerDefender
    • HackerDefender before and after view of file system
    • Detecting HackerDefender with RootkitRevealer
rootkitrevealer limitations
RootkitRevealer Limitations
  • Rootkits have already attacked RKR directly by not cloaking when scanned
    • RKR is given true system view
    • Windows API scan looks like raw scan
  • 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
dealing with rootkits
Dealing with Rootkits
  • Unless you have specific uninstall instructions from an authoritative source:
  • 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

Reformat the system and reinstall Windows!