1. 1 On the Performance of Internet Worm Scanning Strategies Authors: Cliff C. Zou, Don Towsley, Weibo Gong Publication: Journal of Performance Evaluation, 63(7), 700-723, July 2006 Presenter: Cliff Zou for CDA6133, Spring’08

2. 2 Motivation Hackers have tried various scanning strategies in their scan-based worms  Uniform scan  Code Red, Slammer  Local preference scan  Code Red II  Sequential scan  Blaster Possible scanning strategies:  Target preference scan (selective attack from a routing worm)  Divide-and-conquer scan How do they affect a worm’s propagation?  Mean value analysis ( based on law of large number )  Numerical solutions; Simulation studies.

3. 3 Epidemic Model Introduction Model for homogeneous system Model for interacting groups : # of infectious : infection ability : # of hosts : scan rate For worm modeling: : scanning space

4. 4 Infinitesimal Analysis of Epidemic Model From time t to t+  :  Vulnerable hosts [N-I(t)]; infected hosts I(t).  An infected host infects vulnerable hosts.  Negligible of Prob. “two scans hitting the same vulnerable host”.  Newly infected hosts:  Negligible of Prob. “two infected hosts infect the same vulnerable host”. Thus I(t+  ) is : # of hosts : scan rate : scanning space : # of infectious : small time interval Prob. p of a worm copy hitting a specific IP address during  :

5. 5 Uniform Scan Worm Traditional worm: Code Red, Slammer  Uniformly scans the entire IPv4 space (  = 2 32 ) Hit-list worm – increase I(0): [Staniford et al. 2002]  Knowing IP addresses of a fraction of vulnerable hosts.  Has a large number of initially infected hosts I(0). Routing worm – decrease  : [Zou et al. 2003]  Using BGP routing table to only scan BGP routable space.  Currently, only 32% of IPv4 space is routable.  Has a bigger infection ability 

6. 6 Hitlist, routing worm Code Red style worm  = 358/min N = 360,000 hitlist, I(0) = 10,000 routing,  =.29 £ 2 32 Defense: Crucial to prevent attackers from  Identifying IP addresses of a large number of vulnerable hosts  Flash worm, Hit-list worm  Obtaining address information to reduce a worm’s scanning space  Routing worm

7. 7 Local Preference Scan Worm Model: epidemic in interacting groups Analysis: assume K “/n” networks  Prob. p : uniformly scan local “/n” network  Prob. ( 1-p ): uniformly scan others Conclusions:  Vulnerable hosts uniformly distributed:  No difference as long as the worm spreads out to every network.  Vulnerable hosts not uniformly distributed:  Analysis: hosts uniformly distributed in m out of K networks  Local preference scan increases a worm’s speed.

8. 8  Local preference scan increases speed (when vulnerable hosts are not uniformly distributed)  Local scan on Class A ( “/8”) networks: p*  1  Local scan on Class B ( “/16” ) networks: p*  0.85  Code Red II: p =0.5 (Class A), p =0.375 (Class B)  Smaller than p* Local Preference Scan Worm Class A local scan (K=256, m=116) Class B local scan (K=2 16, m=116 £ 2 8 )

9. 9 Sequential Scan Worm Sequential scan:  Sequentially scans IP addresses from a starting point.  Blaster worm selects its starting point locally with p =0.4  Such local preference slows down worm propagation.  Reason: child worm copies are more likely to be wasted on repeating their parents’ scanning trails. Sequential scan is equivalent to uniform scan when  Vulnerable hosts uniformly distributed in IPv4 space.  The worm selects starting point uniformly.

10. 10 Simulations agree with our analyses. Analysis limitation (mean value analysis):  No consideration of variability. Sequential Scan Worm Simulation Study Comparison of uniform scan, sequential scan with/without local preference (100 simulation runs; vulnerable hosts uniformly distributed in entire IPv4 space)

11. 11 Sequential Scan Worm Simulation Study Observations:  Local preference in selecting starting point is a bad idea.  Mean value analysis cannot analyze variability. Uniform scan, sequential scan with/without local preference (100 simulation runs) Vulnerable hosts uniformly distributed in BGP routable IP space (28.6% of IPv4 space)

12. 12 Witty worm modeling Witty’s destructive behavior: 1). Send 20,000 UDP scans to 20,000 IP addresses 2). Write 65KB in a random point in hard disk  Consider an infected computer:  Constant bandwidth  constant time to send 20,000 scans  Random point writing  infected host crashes with prob.  Crashing time approximate by Exponential distribution ( )

13. 13 Witty worm modeling hours Memoryless property : # of crashed infected computers at time t # of vulnerable at t *Witty trace provided by U. Michigan “Internet Motion Sensor”

14. 14 Two Guidelines in Defense Prevent attackers from  Identifying IP addresses of a large number of vulnerable hosts  Flash worm, Hit-list worm  Obtaining address information to reduce a worm’s scanning space  Routing worm Worm monitoring system  IP space coverage is not the only issue  Should monitor as many as possible well distributed IP blocks  non-uniform scan worm

15. 15 Summary Modeling basis:  Law of large number; mean value analysis; infinitesimal analysis.  Epidemic model: Conclusions:  All about worm scanning space   or density of vulnerable population)   Flash worm, Hit-list worm, Routing worm  Local preference, divide-and-conquer, selective attack  Monitoring challenge: sequential scan worm

16. 16 Contributions Provided comprehensive analysis of worm propagation with different scanning strategies  Uniform scan, local preference scan, sequential scan, BGP routing scan, hit-list..  Revealed the underlying connections between different worm scanning strategies  Host distribution, scanning space  Provided several defense guidelines

17. 17 Weaknesses Mean-value analysis, not suitable for small-scale worm propagation Mathematical analysis makes some assumptions  Host uniform distribution, equal scan rate No consideration of topology  Not suitable for email virus, P2P worm, etc. No model on defense systems Didn’t provide practical defense systems  Only basic guidelines, intuitive clear

18. 18 How to improve Stochastic modeling for small-scale propagation Topological modeling Present detailed defense methods