1. 1 On the Performance of Internet Worm Scanning Strategies Cliff C. Zou, Don Towsley, Weibo Gong Univ. Massachusetts, Amherst

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 Some Analysis Conclusions Equivalent when hosts are uniformly distributed  Uniform scan  Sequential scan  Divide-and-conquer scan Local preference scan increases a worm’s speed  When vulnerable hosts are not uniformly distributed  Optimal local scan prob. p  when local network size  Sequential scan  selecting starting point locally slows down worm propagation speed Selective attack  global scan or target-only scan determined by distribution of vulnerable hosts.

4. 4 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

5. 5 Epidemic Model Introduction Model for homogeneous system Model for interacting groups : # of infectious : infection ability : # of hosts : scan rate For worm modeling: : scanning space

6. 6 Infinitesimal Analysis of Epidemic Model From time t to t+  : (  ! 0)  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  :

7. 7 Idealized Worm Know IP addresses of all vulnerable hosts Perfect worm  Cooperation among worm copies Flash worm  No cooperation; random scan Complete infection within seconds

8. 8 Uniform Scan Worm Traditional worm: Code Red, Slammer  Uniformly scans the entire IPv4 space (  = 2 32 ) Hit-list worm: [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: [Zou et al. 2003]  Using BGP routing table to reduce worm scanning space.  Has a bigger infection ability 

9. 9 Uniform Scan Worms Comparison 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 Hit-list worm has a hit-list of I(0)=10,000 Routing worm has  =0.286 £ 2 32 Other parameters: N=360,000  =358/min I(0)=10

10. 10 Divide-and-Conquer Scan Worm Divide-and-conquer scan:  An infected host gives half of its scanning space to its newest infected child host.  At time t, each worm copy has  Scanning space:  Vulnerable hosts:  Use infinitesimal analysis technique. Conclusion: when vulnerable hosts are uniformly distributed, divide-and-conquer scan is equivalent to uniform scan.

11. 11 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.

12. 12  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 )

13. 13 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.

14. 14 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)

15. 15 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)

16. 16 Selective Attack Worm Target domain: Other domains: Target-only scan: Global scan: Conclusion:  Target-only scan is faster when vulnerable hosts are more densely distributed in the target domain than in other domains ( c 1 <c 2 )

17. 17 Worm Monitoring System Design “Network telescope” monitoring system: [Moore 2002]  Observing global Internet activities based on monitored traffic on a small fraction of IP space.  Should monitor as many as possible well distributed IP blocks. Blaster worm simulation and monitoring After low-pass filter Directly monitored data Worm propagation I(t) and monitored data C(t)

18. 18 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: sequential scan worm