1. Hamsa: Fast Signature Generation for Zero-day Polymorphic Worms with Provable Attack Resilience Zhichun Li, Manan Sanghi, Yan Chen, Ming-Yang Kao and Brian Chavez Lab for Internet & Security Technology (LIST) Northwestern University

2. 2 The Spread of Sapphire/Slammer Worms

3. 3 Desired Requirements for Polymorphic Worm Signature Generation Network-based signature generation –Worms spread in exponential speed, to detect them in their early stage is very crucial… However »At their early stage there are limited worm samples. –The high speed network router may see more worm samples… But »Need to keep up with the network speed ! »Only can use network level information

4. 4 Desired Requirements for Polymorphic Worm Signature Generation No existing work satisfies these requirements ! Noise tolerant –Most network flow classifiers suffer false positives. –Even host based approaches can be injected with noise. Attack resilience –Attackers always try to evade the detection systems Efficient signature matching for high-speed links

5. 5 Outline Motivation Hamsa Design Model-based Signature Generation Evaluation Related Work Conclusion

6. 6 Choice of Signatures Two classes of signatures –Content based »Token: a substring with reasonable coverage to the suspicious traffic »Signatures: conjunction of tokens –Behavior based Our choice: content based –Fast signature matching. ASIC based approach can archive 6 ~ 8Gb/s –Generic, independent of any protocol or server

7. 7 Unique Invariants of Worms Protocol Frame –The code path to the vulnerability part, usually infrequently used –Code-Red II: ‘.ida?’ or ‘.idq?’ Control Data: leading to control flow hijacking –Hard coded value to overwrite a jump target or a function call Worm Executable Payload –CLET polymorphic engine: ‘0\x8b’, ‘\xff\xff\xff’ and ‘t\x07\xeb’ Possible to have worms with no such invariants, but very hard Invariants

8. 8 Hamsa Architecture

9. 9 Hamsa Design Key idea: model the uniqueness of worm invariants –Greedy algorithm for finding token conjunction signatures Highly accurate while much faster –Both analytically and experimentally –Compared with the latest work, polygraph –Suffix array based token extraction Provable attack resilience guarantee Noise tolerant

10. 10 Hamsa Signature Generator Core part: Model-based Greedy Signature Generation Iterative approach for multiple worms

11. 11 Outline Motivation Hamsa Design Model-based Signature Generation Evaluation Related Work Conclusion

12. 12 Problem Formulation Signature Generator Signature false positive bound  Maximize the coverage in the suspicious pool False positive in the normal pool is bounded by  Suspicious pool Normal pool With noise NP-Hard!

13. 13 Model Uniqueness of Invariants FP 21% 9% 17% 5% t1t1 Joint FP with t 1 2% 0.5% 1% t2t2 The total number of tokens bounded by k* U(1)=upper bound of FP( t 1 ) U(2)=upper bound of FP( t 1,t 2 )

14. 14 Signature Generation Algorithm (82%, 50%) (COV, FP) (70%, 11%) (67%, 30%) (62%, 15%) (50%, 25%) (41%, 55%) (36%, 41%) (12%, 9%) u(1)=15% Suspicious pool tokens token extraction Order by coverage t1t1

15. 15 (82%, 50%) (COV, FP) (70%, 11%) (67%, 30%) (62%, 15%) (50%, 25%) (41%, 55%) (36%, 41%) (12%, 9%) t1t1 Order by joint coverage with t 1 (69%, 9.8%) (COV, FP) (68%, 8.5%) (67%, 1%) (40%, 2.5%) (35%, 12%) (31%, 9%) (10%, 0.5%) u(2)=7.5% t2t2 Signature Signature Generation Algorithm

16. 16 Algorithm Analysis Runtime analysis O(T*(|M|+|N|)) Provable Attack Resilience Guarantee –Analytically bound the worst attackers can do! –Example: K*=5, u(1)=0.2, u(2)=0.08, u(3)=0.04, u(4)=0.02, u(5)=0.01 and  =0.01 –The better the flow classifier, the lower are the false negatives Noise ratioFP upper boundFN upper bound 5%1%1.84% 10%1%3.89% 20%1%8.75%

17. 17 Attack Resilience Assumptions Two Common assumptions for any sig generation sys Two Unique assumptions for token-based schemes Attacks to the flow classifier –Our approach does not depend on perfect flow classifiers –With 99% noise, no approach can work! –High noise injection makes the worm propagate less efficiently. Enhance flow classifiers

18. 18 Improvements to the Basic Approach Generalizing Signature Generation –use scoring function to evaluate the goodness of signature Iteratively use single worm detector to detect multiple worms –At the first iteration, the algorithm find the signature for the most popular worms in the suspicious pool. –All other worms and normal traffic treat as noise.

19. 19 Outline Motivation Hamsa Design Model-based Signature Generation Evaluation Related Work Conclusion

20. 20 Experiment Methodology Experiential setup: –Suspicious pool: »Three pseudo polymorphic worms based on real exploits (Code-Red II, Apache-Knacker and ATPhttpd), »Two polymorphic engines from Internet (CLET and TAPiON). –Normal pool: 2 hour departmental http trace (326MB) Signature evaluation: –False negative: 5000 generated worm samples per worm –False positive: »4-day departmental http trace (12.6 GB) »3.7GB web crawling including.mp3,.rm,.ppt,.pdf,.swf etc. »/usr/bin of Linux Fedora Core 4

21. 21 Results on Signature Quality Single worm with noise –Suspicious pool size: 100 and 200 samples –Noise ratio: 0%, 10%, 30%, 50%, 70% –Noise samples randomly picked from the normal pool –Always get above signatures and accuracy. Multiple worms with noises give similar results Worms Training FN Training FP Evaluation FN Evaluation FP Binary evaluation FP Signature Code-Red II 00000 {'.ida?': 1, '%u780': 1, ' HTTP/1.0\r\n': 1, 'GET /': 1, '%u': 2} CLET00.109%00.06236%0.268% {'0\x8b': 1, '\xff\xff\xff': 1,'t\x07\xeb': 1}

22. 22 Speed Results Implementation with C++/Python –500 samples with 20% noise, 100MB normal traffic pool, 15 seconds on an XEON 2.8Ghz, 112MB memory consumption Speed comparison with Polygraph –Asymptotic runtime: O(T) vs. O(|M| 2 ), when |M| increase, T won’t increase as fast as |M|! –Experimental: 64 to 361 times faster (polygraph vs. ours, both in python)

23. 23 Outline Motivation Hamsa Design Model-based Signature Generation Evaluation Related Work Conclusion

24. 24 Related works HamsaPolygraphCFGPADSNemeanCOVERSMalware Detection Network or host based Network Host Content or behavior based Content based Behavior based Content based Behavior based Noise tolerance YesYes (slow) YesNo Yes Multi worms in one protocol YesYes (slow) YesNoYes On-line sig matching Fast SlowFast Slow GeneralityGeneral purpose Protocol specific Server specific General purpose Provable atk resilience YesNo Information exploited   

25. 25 Conclusion Network based signature generation and matching are important and challenging Hamsa: automated signature generation –Fast –Noise tolerant –Provable attack resilience –Capable of detecting multiple worms in a single application protocol Proposed a model to describe the worm invariants

26. Questions ?