Self-Stopping Worms Justin Ma, Geoffrey M. Voelker, Stefan Savage Collaborative Center for Internet Epidemiology and Defenses (CCIED) Department of Computer Science and Engineering University of California, San Diego

2 Worm Epidemic Aftermath Belief: identifying infected hosts easy Expectation: infection activity continues long after the fact Self-stopping worms can evade existing worm treatment techniques

3 State of Affairs Zotob: 1 week Witty: 1 day

4 State of Affairs Opportunity to prevent/contain is short Real-world responses focus on treatment Slammer: 10 minutes Staniford et al.: a few seconds Zotob: 1 week Witty: 1 day

5 State of Affairs Opportunity to prevent/contain is short Real-world responses focus on treatment From: To: Dear Hapless, (hapless.ucsd.edu, 00:0f:ca:c0:e6:64, HAPLESS_WIN2K) appears to be infected with a worm and is scanning external networks on port 445 in violation of University policy. The machine has been blocked at the campus border until it can be cleaned up, secured, and made fully compliant with the Minimum Network Security Standards (see ). Pursuant to UCSD policy concerning compliance with California State Bill 1386 ( if "personal identity information" exists on this machine, that fact must be reported to Sincerely, Academic Computing Services / Network Security

6 State of Affairs Opportunity to prevent/contain is short Real-world responses focus on treatment Just need to know when all hosts infected Why spew?

7 State of Affairs Opportunity to prevent/contain is short Real-world responses focus on treatment Self-stop gives malware many advantages Just need to know when all hosts infected Self-stop

8 Difficulty of Self-Stop How hard with random scanning worms? Gossip-style communication –Opportunistic contact –Conform to probe traffic pattern Without a priori knowledge –E.g., no need to know vulnerability density Perform as well as strategies with a priori knowledge

9 Self-Stopping Worm Design Primary Goal: stop after infecting x% vulnerables –Infect as many as possible Accuracy: ability to meet Primary Goal –At least >= 85% vulnerables Speed: time to reach x% vulnerables –Spread as quickly as possible (beat containment) Duration: time until last host deactivates –Stop as quickly as possible (minimize containment window) Scan traffic –Not focusing on stealthy (tradeoff w/ speed/duration) Ease of implementation/parameterization –Piggy-back over uniform random scanning –No a priori knowledge of vulnerable population

10 Dynamic Estimation Do individual nodes need a priori knowledge? –Size of vulnerable population N –Infected count over time I(t) Worm has an oracle –Know N and I (stop when I(t)/N reaches goal) Increasingly practical –Know N (locally estimate I(t) knowing N) –Sum-Count (locally estimate N) –Sum-Count-X (collaborate to estimate N)

11 Simulation Methodology Modify random scanning worms –32-bit address space –130,000 vulnerables (we tried other values too) –Each host, 4000 scans per timestep –Slammer: >= 75,000 vulnerable, ~4000 scans/s [Moore et al., “Inside the Slammer Worm”, 2003] Universal reachability No network latency or congestion Start w/ one infected host Scan in rounds

12 Know-NI Perfect knowledge lets worms stop on a dime

13 Estimating I(t) from N Directly observing I(t) is difficult Restricted to only knowing N? –Observe through netcraft.com, port scanning I(t) = f(N, r, t) –Based on analytic model for epidemics –r is per-host scan rate –See paper for details

14 Estimating I(t) from N Only knowing N, worms can still stop quickly

15 Local Estimation Estimate N on-the-fly –General-purpose self-stop –No need to gather a priori intelligence Scanning = Sampling w/ Replacement –Hits on Vulnerables = Successes –Total Scans = Trials N est = 2 32 * (Hits / Scans)

16 Hits: 0 Scans: 1Hits: 0 Scans: 0Hits: 1 Scans: 2 Sum-Count Estimate N through local estimation Hits: 1 Scans: 3 33% hosts vulnerable

17 Sum-Count More than 2x longer to stop… Local sampling alone insufficient

18 Why Sum-Count Fails Variance[N est ]  1 / Scans Many infected nodes too unlucky/new Reduce error by increasing scans without increasing scan rate Sum-Count-X –Aggregate samples (scans) –Opportunistic exchange –Distributed sampling by combining host estimates

19 Sum-Count-X Collaborative estimation via exchange Hits: 1 Scans: 3 Hits: 2 Scans: 3 Hits: 3 Scans: 6 Hits: 1 Scans: 2Hits: 0 Scans: 1 Hits: 1 Scans: 2Hits: 0 Scans: % hosts vulnerable +

20 Sum-Count-X Similar result without perfect knowledge!

21 Why Sum-Count-X Succeeds Combines local estimation with exchange Leverages “experience” of older hosts

22 Summary 20 simulation runs each Speed (to 85%)Duration Strategy50th90th50th90th Greedy Know-NI Know-N Sum-Count Sum-Count-X Spreads quicklyStops quickly

23 Conclusions Self-stopping worms –Easy to write –Advance knowledge of vulnerable host population is unnecessary to be successful –Sum-Count-X demonstrates these points Implications for future defenses –Cannot depend on simple identification –Need new ways to identify/treat –If those fail, containment is even more critical

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25 More in Paper Basic Heuristics –From epidemic protocol literature Dynamic Estimation with Bitmaps Permutation Scanning Scan Traffic

26 Infected Count

27 Sum-Count-Push

28 Nematodes Aka “good worms” Xerox PARC [Shoch and Hupp, 1980] Prevent nematodes from spreading out of control Utility not so convincing