Monitoring and Early Warning for Internet Worms

1. Monitoring and Early Warning for Internet Worms Authors: Cliff C. Zou, Lixin Gao, Weibo Gong, Don Towsley Univ. Massachusetts, Amherst Publish: 10th ACM Conference on Computer and Communication Security (CCS'03), 2003 Presenter: Cliff C. Zou (01/12/2006)

2. Monitored traffic How to detect an unknown worm at its early stage? • Monitor: • Worm scans to unused IPs • TCP/SYN packets • UDP packets Internet • Monitored data is noisy Unused IP space Local network

3. Reflection • Worm anomaly  other anomalies? • A worm has its own propagation dynamics • Deterministic models appropriate for worms Can we take advantage of worm model to detect a worm?

4. 2% 1% Worm model in early stage Initial stage exhibits exponential growth

5. Worm traffic “Trend Detection”  Detect traffic trend, not burst Trend: wormexponential growth trend at the beginning Detection: the exponential rate should be a positive, constant value Monitored illegitimate traffic rate Exponential rate a on-line estimation Non-worm traffic burst

6. Why exponential growth at the beginning? • The law of natural growth reproduction • When interference is negligible (beginning phase) • Attacker’s incentive: infect as many as possible before people’s counteractions • If not, a worm does not reach its spreading speed limit • Slow spreading worm detected by other ways • Security experts manual check • Honeypot, …

7. Zt: # of monitored scans at time t : monitoring noise yield Model for estimate of wormexponential growth rate a Exponential model:

8. Estimation by Kalman Filter System: where Kalman Filter for estimation of Xt :

9. Code Red simulation experiments Population: N=360,000, Infection rate: a = 1.8/hour, Scan rate h = N(358/min, 1002), Initially infected: I0=10 Monitored IP space 220, Monitoring interval: 1 minute Consider background noise At 0.3% (157 min): estimate stabilizes at a positive constant value

10. yield Damage evaluation—Prediction of global vulnerable population N Accurate prediction when less than 1% of N infected

11. : Prob. an infected to be observed by the monitor in a unit time Monitoring 214 IP space (p=4£ 10-6) # of newly observed (tt+1) # of unobserved Infected by t Damage evaluation — Estimation of global infected population It : cumulative # of observed infected hosts by time t : per host scan rate : fraction of address space monitored

12. What’s the paper’s contribution? • A novel approach in anomaly detection • Popular approach is based on static threshold • Paper exploits worm dynamics • Dynamics in a series of time • Worm potential damage prediction • Estimate global infected based on local info • Predict global vulnerable population

13. Why this paper can be published? • Different approach from popular ways • Model-based anomaly detection • Fresh view point --- interesting • Solid (fancy) mathematic background • Math is appropriate • A pure experimental report is not (good) enough for academic paper • Timely appearance • Catch a promising/hot topic ASAP • Rely on: advisors, (conference) paper, tech news, colleagues,

14. What’s the paper’s weakness? • Early detection provides limited information • Does not provide signature for worm defense • Does not (accurately) identify global infected hosts • Require a large empty IP space for monitoring • Not very good for individual local network • Worm damage prediction results are accurate only for uniform-scan worms • Many worms using biased scanning strategies

15. How to improve the paper? • I have improved CCS’03 conference paper and published in IEEE Tran. on Networking • Detect a worm earlier • Conference paper uses simple worm model, TON’s uses exponential model (several times faster) • Consider the limitation of monitoring system • TON’s paper adds analysis/experiments of the monitoring problem for non-uniform scan worms