Internet Quarantine: Requirements for
Containing Self-Propagating Code
David Moore et. al.
University of California,
San Diego
Internet Quarantine: Requirements for
Containing Self-Propagating Code
Aleksandar Kuzmanovic
Rice University,
COMP 629
Outline

Background about worm, esp. Code-Red
–
–
What’s worm, esp. Code-Red
Prevention, Treatment and Containment of the worm.

SI epidemic model and Code Red propagation model.

Simulations on Code Red Propagation and Containment
System Deployment.

Conclusion.
Background: what is worm?

Worm is a self-replicating software designed
to spread through the network.

Worm vs Virus and Trojan horse
–
–
Virus and Trojan horse rely on human intervention
to spread.
Worm is autonomous.
Background: Code-Red v1

Outbreak: June 18, 2001

How it works:
–
–
–
Buffer overflow exploit on Microsoft IIS web server.
Upon infected a machine, randomly generate a list of IP addresses.
Probe each of the addresses from the list.

Payload: DDoS attack against www1.whitehouse.gov.

Damage: little
–
Fixed random seed.
Background: Code-Red v2

Outbreak: July 19, 2001

How it works:
–
–

Similar to Code-Red v1, but with a random seed.
Generates 11 probes for second.
Damage: severe
–
359,000 machines were infected within 14 hours.
How to mitigate the threat of worms(1)

Three approaches
–
Prevention:



Reduce the size of the vulnerable population.
E.g. A single vulnerability in a popular software system
can result in millions of vulnerable hosts.
E.g. Code Red attacks millions of MS IIS web server.
How to mitigate the threat of worms (2)

Treatment:
–
–

E.g. virus scanner.
The time required to design, develop and test a security
flaw is usually for too slow than the spread of the worm.
Containment:
–
–
E.g. firewall, filters
Containment is used to protect individual networks, and
isolate infected hosts.
SI Model (1)







In this work, a vulnerable machine is described as susceptible (S)
machine.
A infected machine is described as infected (I).
Let N be the number of vulnerable machines.
Let S(t) be the number of susceptible host at time t, and s(t) be
S(t)/N, where N = S(t) + I(t).
Let I(t) be the number of infected hosts at time t, and i(t) be I(t)/N.
Let  be the contact rate of the worm.
Define:
dI
IS

dt
N
dS
IS
 
dt
N
SI Model (2)
di
SI 1

    s (t )  i (t )
dt
N N
di
  i (1  i )
dt
Solving the differential equation:
e  (t T )
i (t ) 
1  e  (t T )
where T is a constant
Code Red Propagation Model (1)



Code Red generates IPv4 address by
random. Thus, there are totally 2^32
addresses.
Let r be the probe rate of a Code Red worm.
Thus:
N
  r 32
2
Code Red Propagation Model (2)

Two problems
–
Cannot model preferential targeting algorithm.

–
E.g. select targets form address ranges closer to the
infected host.
The rate  only represents average contact rate.

E.g. a particular epidemic may grow significantly more
quickly by making a few lucky targeting decisions in
early phase.
Code Red Propagation Model (3)

Example on 100 simulations on Code Red propagation model:
After 4 hours:
55% on average
80% in 95th percentiles
25% in 5th percentiles
Modeling Containment Systems (1)

A containment system has three important
properties:
–
Reaction time – the time necessary for



Detection of malicious activity,
Propagation of the containment information to all hosts
participating the system, and
Activating any containment strategy.
Modeling Containing Systems (2)
–
Containing Strategy

Address blacklisting
–
Maintain a list of IP addresses that have been identified as
being infected.
– Drop all the packets from one of the addresses in the list.
– E.g. Mail filter.
– Advantage: can be implemented easily with existing
firewall technology.
Modeling Containing Systems (3)

Content filtering
–
Requires a database of content signatures known to
represent particular worms.
– This approach requires additional technology to
automatically create appropriate content signatures.
– Advantage: a single update is sufficient to describe any
number of instances of a particular worm implementation.

Deployment scenarios
–
–
–
Ideally, a global deployment is preferable.
Practically, a global deployment is impossible.
May be deploying at the border of ISP networks.
Idealized Deployment (1)

Simulation goal
–

Simulation Parameters:
–
–

To find how short the reaction time is necessary to effectively
contain the Code-Red style worm.
360,000 vulnerable hosts out of 232 hosts.
Probe rate of a worm : 10 per sec.
Containment strategy implementation
–
Address blacklisting

–
Send IP addresses to all participating hosts.
Content filtering

Send signature of the worm to all participating hosts.
Idealized Deployment (2)

Result: content filtering is more effective.
Number of
susceptible
host decreases
20 min
Worms
unchecked
2 hr
Idealized Deployment (3)

Next goal:
–
–
To find the relationship between containment
effectiveness and worm aggressiveness.
Figures are in log-log scale.
Idealized Deployment (4)
Percentage of infected hosts
Address blacklisting is hopeless
when encountering aggressive worms.
Practical Deployment (1)

Network Model
–
AS sets in the Internet:


–
routing table on July 19,2001
1st day of the Code Red v2 outbreak.
A set of vulnerable hosts and ASes:


Use the hosts infected by Code Red v2 during the initial
24 hours of propagation.
A large and well-distributed set of vulnerable hosts.
–
338,652 hosts distributed in 6,378 ASes.
Practical Deployment (2)

Deployment Scenarios
–
–
Use content filtering only.
Filtering firewall are deployed on the borders of
both the customer networks, and ISP’s networks.
Deployment of containment strategy.
Practical Deployment (3)

Reaction time: 2hrs
Difference in
performance
because of the
difference in path
coverage.
Practical Deployment (4)
System fails to contain
the worm.
Conclusion

Explore the properties of the containment system
–
–
–

Reaction time
Containment strategy
Deployment scenario
In order to contain the worm effectively
–
–
–
Require automated and fast methods to detect and react to
worm epidemics.
Content filtering is the most preferable strategy.
Have to cover all the Internet paths when deploying the
containment systems.