UCSD Potemkin Honeyfarm - PowerPoint PPT Presentation

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UCSD Potemkin Honeyfarm


Erin Kenneally, Justin Ma, David Moore, Stefan Savage, ... George Varghese, Geoff Voelker, Michael Vrable. Network Telescopes ... – PowerPoint PPT presentation

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Title: UCSD Potemkin Honeyfarm

UCSD Potemkin Honeyfarm
  • Jay Chen, Ranjit Jhala, Chris Kanich,
  • Erin Kenneally, Justin Ma, David Moore, Stefan
  • Colleen Shannon, Alex Snoeren, Amin Vahdat, Erik
  • George Varghese, Geoff Voelker, Michael Vrable

Network Telescopes
  • Infected host scans for other vulnerable hosts by
    randomly generating IP addresses
  • Network Telescope monitor large range of unused
    IP addresses will receive scans from infected
  • Very scalable. UCSD monitors 17M addresses (/8

Telescopes Active Responders
  • Problem Telescopes are passive, cant respond to
    TCP handshake
  • Is a SYN from a host infected by CodeRed or
    Welchia? Dunno.
  • What does the worm payload look like? Dunno.
  • Solution proxy responder
  • Stateless TCP SYN/ACK (Internet Motion Sensor),
    per-protocol responders (iSink)
  • Stateful Honeyd
  • Can differentiate and fingerprint payload

  • Problem dont know what worm/virus would do?
  • No code ever executes after all.
  • Solution redirect scans to real infectible
    hosts (honeypots)
  • Individual hosts or VM-based Collapsar,
    HoneyStat, Symantec
  • Can reduce false positives/negatives with
    host-analysis (e.g., TaintCheck, Vigilante,
    Minos) and behavioral/procedural signatures
  • Challenges
  • Scalability
  • Liability (honeywall)
  • Isolation (2000 IP addrs -gt 40 physical machines)
  • Detection (VMWare detection code in the wild)

The Scalability/Fidelity tradeoff
Telescopes Responders (iSink, Internet Motion
VM-based Honeynet
Network Telescopes (passive)
Live Honeypot
Highest Fidelity
Most Scalable
Potemkin A large scale high-fidelity honeyfarm
  • Goal emulate significant fraction of Internet
    hosts (10M)
  • Multiplex large address space on smaller of
  • Temporal spatial multiplexing
  • Scalability, Fidelity, and Containment in the
    Potemkin Virtual Honeyfarm, Vrable, Ma, Chen,
    Moore, VandeKieft, Snoeren, Voelker, and Savage,
    SOSP 2005

UCSD Honeyfarm Approach
  • Make VMs very, very cheap
  • Create one (or more) VM per packet on demand
  • Deploy many types of VM systems
  • Plethora of OSes, versions, configurations
  • Monitor VM behavior
  • Decide benign or malicious
  • Benign Quickly terminate, recycle resources
  • Malicious Track propagation, save for offline
    analysis, etc.
  • Assumes common case that most traffic is benign
  • Key issues for remainder of talk
  • 1) Scaling
  • 2) Containment

  • Naïve approach one machine per IP address
  • 1M addresses 1M hosts 2B investment
  • However most of these resources would be wasted
  • Claim should be possible to make do with 5-6
    orders of magnitude less

Resulting philosophy
  • Only commit the minimal resources needed and only
    when you need them
  • Address space multiplexing
  • Late-bind the assignment of IP addresses to
    physical machines (on demand assumption of
  • Physical resource multiplexing
  • Multiple VMs per physical machine
  • Exploit memory coherence
  • Delta virtualization (allows 1000 VMs per
    physical machine)
  • Flash cloning (low latency creation of on demand

Address space multiplexing
  • For a given unused address range and service time
    distribution, most addresses are idle

/16 network 500ms service time But most of these
arehorizontal port scans!
The value of scan filtering
  • Heuristic no more than one (srcip, dstport,
    protocol) tuple per 60 seconds

  • Gateway (Click-based) terminates inbound GRE
  • Maintains external IP address-gttype mapping
  • i.e. should be a Windows XP box w/IIS
    version 5, etc
  • Mapping made concrete when packet arrives
  • Flow entry created and pkt dispatched to
    type-compatible physical host
  • VMM on host creates new VM with target IP address
  • VM and flow mapping GCd after system determines
    that no state change
  • Bottom line 3 orders of magnitude savings

Physical resource multiplexing
  • Can create multiple VMs per host, but expensive
  • Memory address spaces for each VM (100s of MB)
  • In principal limit for VMWare 64 VMs, practical
    limit less
  • Overhead initializing new VM wasteful
  • Claim can support 100s-1000 VMs per host by
    specializing hosts and VMM
  • Specialize each host to software type
  • Maintain reference image of active system of that
  • Flash cloning instantiate new VMs via copying
    reference image
  • Delta virtualization share state COW for new VMs
    (state proportional to difference from reference

How much unique memory does a VM need?
Potemkin VMM implementation
  • Xen-based using new shadow translate mode
  • New COW architecture being incorporated back into
    Xen (VT compatible)
  • Clone manager instantiates frozen VM image and
    keeps it resident in physical memory
  • Flash clone memory instantiated via eager copy of
    PTE pages and lazy faulting of data pages(moving
    to lazy profile driven eager pre-copy)
  • Ram disk or Parallax FS for COW disks
  • Overhead currently takes 300ms to create new VM
  • Highly unoptimized (e.g. includes python
  • Goal Pre-allocated VMs can be invoked in 5ms

  • Key issue 3rd party liability and contributory
  • Honeyfarm worm accelerator
  • Worse, I knowingly allowed my hosts to be
    infected (premeditated negligence)
  • Export policy tradeoffs between risk and fidelity
  • Block all outbound packets no TCP connections
  • Only allow outbound packets to host that
    previously send packet no outbound DNS, no
    botnet updates
  • Allow outbound, but scrub is this a best
  • In the end, need fairly flexible policy
  • Could do whole talk on interaction between
    technical legal drivers
  • But it gets more complex

Internal reflection
  • If outbound packet not permitted to real
    internet, it can be sent back through gateway
  • New VM generated to assume target address
    (honeyfarm emulates external Internet)
  • Allows causal detection (A-gtB-gtC-gtD) and can
    dramatically reduces false positives
  • However, creates new problem
  • Is there only one version of IP address A?
  • Yes, single universe inside honeyfarm
  • No isolation between infections
  • Also allows cross contamination (liability rears
    its head again)
  • No, how are packets routed internally?

Causal address space aliasing
  • A new packet i destined for address t, creates a
    new universe Uit
  • Each VM created by actions rooted at t is said
    to exist in the same universe and a single export
    policy is shared
  • In essence, the 32-bit IP address space is
    augmented with a universe-id that provides
  • Universes are closed no leaking
  • What about symbiotic infections? (e.g., Nimda)
  • When a universe is created it can be made open it
    to multiple outside influences
  • Common use a fraction of all traffic is directed
    to a shared universe with draconian export rules

Overall challenges for honeyfarms
  • Depends on worms scanning it
  • What if they dont scan that range (smart bias)
  • What if they propagate via e-mail, IM? (doable,
    but privacy issues)
  • Camouflage
  • Honeypot detection software exists perfect
    virtualization tough
  • It doesnt necessary reflect whats happening on
    your network (cant count on it for local
  • Hence, there is a need for both honeyfarm and
    in-situ approaches

  • Potemkin High-fidelity, scalable honeyfarm
  • Fidelity New virtual host per packet
  • Scalability 10M IP addresses ? 100 physical
  • Approach
  • Address multiplexing late-bind IPs to VMs
  • Physical multiplexing VM coherence, state
  • Flash cloning Clone from reference image
  • Delta virtualization Copy-on-write memory, disk
    (100 VMs per host)
  • Containment
  • Risk vs. fidelity Rich space of export policies
    in gateway
  • Challenges
  • Attracting attacks, camouflage, denial-of-service
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