High-Performance Network Anomaly/Intrusion Detection & Mitigation System (HPNAIDM)

1. High-Performance Network Anomaly/Intrusion Detection & Mitigation System (HPNAIDM) Yan Chen Lab for Internet & Security Technology (LIST) Department of Electrical Engineering and Computer Science Northwestern University http://list.cs.northwestern.edu

2. The Spread of Sapphire/Slammer Worms

3. Current Intrusion Detection Systems (IDS) • Mostly host-based and not scalable to high-speed networks • Slammer worm infected 75,000 machines in <10 mins • Host-based schemes inefficient and user dependent • Have to install IDS on all user machines ! • Mostly simple signature-based • Cannot recognize unknown anomalies/intrusions • New viruses/worms, polymorphism

4. Current Intrusion Detection Systems (II) • Statistical detection • Unscalable for flow-level detection • IDS vulnerable to DoS attacks • Overall traffic based: inaccurate, high false positives • Cannot differentiate malicious events with unintentional anomalies • Anomalies can be caused by network element faults • E.g., router misconfiguration, link failures, etc.

5. HPNAIDM system HPNAIDM system Internet scan port Internet LAN Internet LAN HRAID system LAN Switch Switch Splitter Switch Splitter Router Router Switch Switch Router scan port LAN LAN Switch LAN (a) HPNAIDM system (b) (c) High-Performance Network Anomaly/Intrusion Detection and Mitigation System (HPNAIDM) • Attached to a router/switch as a black box • Edge network detection particularly powerful Monitor each port separately Monitor aggregated traffic from all ports Original configuration

6. Features of HPNAIDM • Online traffic recording [ACM SIGCOMM IMC 2004, IEEE INFOCOM 2006] • Reversible sketch for data streaming computation • Record millions of flows (GB traffic) in a few hundred KB • Small # of memory access per packet • Scalable to large key space size (232 or 264) • Online sketch-based flow-level anomaly detection [IEEE ICDCS 2006] [IEEE CG&A, Security Visualization 06] • Adaptively learn the traffic pattern changes • As a first step, detect TCP SYN flooding, horizontal and vertical scans even when mixed

7. Features of HPNAIDM (II) Integrated approach for false positive reduction • Polymorphic worm detection (Hamsa) [IEEE Symposium on Security and Privacy 2006] • Network element fault Diagnostics with Operational Determinism (ODD) [ACM SIGMETRICS 2006, poster paper] HPNAIDM: First flow-level intrusion detection that can sustain 10s Gbps bandwidth even for worst case traffic of 40-byte packet streams Patents have been filed or are currently being filed for most technologies.

8. HPNAIDM Architecture Remote aggregated sketch records Sent out for aggregation Part I Sketch-based monitoring & detection Reversible sketch monitoring Normal flows Sketch based statistical anomaly detection (SSAD) Local sketch records Streaming packet data Keys of suspicious flows Filtering Keys of normal flows Polymorphic worm detection (Hamsa) Signature-based detection Per-flow monitoring Suspicious flows Part II Per-flow monitoring & detection Network fault diagnosis (ODD) Intrusion or anomaly alarms Modules on the critical path Modules on the non-critical path Data path Control path

9. Research methodology Combination of theory, synthetic/real trace driven simulation, and real-world implementation and deployment

10. Hamsa: Fast Signature Generation for Zero-day Polymorphic Wormswith Provable Attack Resilience Zhichun Li, Manan Sanghi, Yan Chen, Ming-Yang Kao and Brian Chavez Northwestern University

11. Desired Requirements for Polymorphic Worm Signature Generation • Network based, no host-level info • Noise tolerant • Most network flow classifiers suffer false positives. • Even host based IDSes, such as honeynets, can be injected with noise. • Attack resilience • Attackers always try to evade the IDS • Efficient signature matching for high-speed links No existing work satisfies these requirements !

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

13. Choice of Signatures • Two classes of signatures • Content based • Token: a substring with reasonable coverage to the suspicious traffic • Signatures: conjunction and/or sequence of tokens • Behavior based • Our choice: content based • Fast signature matching. ASIC based approach can archive 6 ~ 8Gb/s • Generic, not depend upon any protocol or server

14. Unique Invariants of Worms • Protocol Frame • Makes server branch down 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 • Example: ATPhttpd exploit, wu-ftp exploit • 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

15. Hamsa Architecture

16. 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 • Use less than 20% space, but at least 20 times faster • Provable attack resilience guarantee • Propose an adversary model • Noise tolerant

17. Hamsa Signature Generator • Core part: Model-based Greedy Signature Generation • Iterative approach for multiple worms • Signature refinement for better specificity • False positive is worse than false negative

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

19. Problem Formulation Noisy Token Multiset Signature Generation Problem :INPUT: Suspicious pool M and normal traffic pool N; value r<1.OUTPUT: A multi-set of tokens signature S={(t1, n1), . . . (tk, nk)} such that the signature can maximize the coverage in the suspicious pool and the false positive in normal pool should less than r • Without noise, exist polynomial time algo • With noise, NP-Hard

20. Model Uniqueness of Invariants • Let worm has a set of invariants:Determine their order by: t1: the token with minimum false positive in normal traffic. u(1) is the upper bound of the false positive of t1 t2: the token with minimum joint false positive with t1 FP({t1,t2}) bounded by u(2) ti: the token with minimum joint false positive with {t1, t2, ti-1}. FP({t1,t2,…,ti}) bounded by u(i) The total number of tokens bounded by k*

21. (COV, FP) (82%, 50%) (70%, 5%) (67%, 30%) (62%, 15%) (50%, 25%) (41%, 55%) (36%, 41%) (12%, 9%) Signature Generation Algorithm token extraction t1 u(1)=10% tokens Suspicious pool Order by coverage

22. (COV, FP) (COV, FP) (82%, 50%) (69%, 4.8%) (68%, 4.5%) (70%, 5%) (67%, 1%) (67%, 30%) (40%, 2.5%) (62%, 15%) (35%, 12%) (50%, 25%) (41%, 55%) (31%, 9%) (36%, 41%) (10%, 0.5%) (12%, 9%) Signature Generation Algorithm Signature t1 t2 u(2)=2% Order by joint coverage with t1

23. Algorithm Analysis • Runtime analysis O(T*(|M|+|N|)) • Provable Attack Resilience Guarantee • Analytically bound the worst attackers can do! • False negative: • Example: K*=5, u(1)=0.2, u(2)=0.08, u(3)=0.04, u(4)=0.02 and u(5)=0.01 • The better the flow classifier, the lower are the false negatives

24. Attack Resilience Assumptions • Common assumptions for any sig generation sys • The attacker cannot control which worm samples are encountered by Hamsa • The attacker cannot control which worm samples encountered will be classified as worm samples by the flow classifier • Unique assumptions for token-based schemes • The attacker cannot change the frequency of tokens in normal traffic • The attacker cannot control which normal samples encountered are classified as worm samples by the worm flow classifier

25. Improvements to the Basic Approach • Generalizing signature generation • Provide the flexibility and tradeoff between signature coverage and false positives • Define scoring function: score(cov, fp, …) 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.

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

27. Experiment Methodology • Experiential setup: • Suspicious pool: • Three pseudo polymorphic worm based on real exploits (Code-Red II, Apache-Knacker and ATPhttpd), • Two polymorphic engine 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

28. 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 signature and accuracy, except in the next slide

29. Results on Signature Quality (II) • Suspicious pool with high noise ratio: • For noise ratio 50% and 70%, sometimes we can produce two signatures, one is the true worm signature, anther solely from noise. • The false positive of these noise signatures have to be very small: • Mean: 0.09% • Maximum: 0.7% • Multiple worms with noises give similar results

30. Speed Results • Implementation with hybrid of C++/Python • 500 samples with 20% noise, 326MB normal traffic pool, 15 seconds on an XEON 2.8Ghz, 50MB 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) Speed up ratio

31. Related works

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

33. Backup Slides

34. Motivation: 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

35. Token-fit Attack Can Fail Polygraph • Polygraph: hierarchical clustering to find signatures w/ smallest false positives • Attacker can potentially obtain the token distribution of the noise in the suspicious pool • He can make the worm samples more like noise traffic • Different worm samples encode different noise tokens • Our approach can still work!

36. Noise samples Worm samples N1 W1 N2 W2 N3 W3 Merge Candidate 3 Merge Candidate 2 Merge Candidate 1 Token-fit attack could make Polygraph fail CANNOT merge further!NO true signature found!

37. Generalizing Signature Generation with noise • BEST Signature = Balanced Signature • Balance the sensitivity with the specificity • But how? Create notation Scoring function:score(cov, fp, …) to evaluate the goodness of signature • Current used • Intuition: it is better to reduce the coverage 1/a if the false positive becomes 10 times smaller. • Add some weight to the length of signature (LEN) to break ties between the signatures with same coverage and false positive

38. Extension to multiple worm • Iteratively use single worm detector to detect multiple worm • 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. • Though the analysis for the single worm can apply to multiple worms, but the bound are not very promising. Reason: high noise ratio

39. Experiment: Sample requirement • Coincidental-pattern attack [Polygraph] • Results • For the three pseudo worms, 10 samples can get good results. • CLET and TAPiON at least need 50 samples • Conclusion • For better signatures, to be conservative, at least need 100+ samplesRequire scalable and fast signature generation!

40. Implementation details • Token Extraction: extracta set of tokens with minimum length l and minimum coverage COVmin. • Polygraph use suffix tree based approach: 20n space and time consuming. • Our approach: Enhanced suffix array 4n space and much faster! (at least 20 times) • Calculate false positive when check U-bounds • Again suffix array based approach, but for a 300MB normal pool, 1.2GB suffix array still large! • Optimization: using MMAP, memory usage: 150 ~ 250MB

41. Token Extraction • Extracta set of tokens with minimum length lmin and coverage COVmin. And for each token output the frequency vector. • Polygraph use suffix tree based approach: 20n space and time consuming. • Our approach: • Enhanced suffix array 4n space • Much faster, at least 50(UPDATE) times! • Can apply to Polygraph also.

42. Calculate the false positive • We need to have the false positive to check the U-bounds • Again suffix array based approach, but for a 300MB normal pool, 1.2GB suffix array still large! • Improvements • Caching • MMAP suffix array. True memory usage: 150 ~ 250MB. • 2 level normal pool • Hardware based fast string matching • Compress normal pool and string matching algorithms directly over compressed strings

43. Experiment: Attacks • We propose a new attack: token-fit. • The attacker may study the noise inside the suspicious pool • Create worm sample Wi which may has more same tokens with some normal traffic noise sample Ni • This will stuck the hierarchical clustering used in [Polygraph] • BUT We still can generate correct signature!

44. Experiment: U-bound evaluation • To be conservative we chose k*=15. • Even we assume every token has 70% false positive, their conjunction still only have 0.5% false positive. In practice, very few tokens exceed 70% false positive. • Define u(1) and ur, generate • We tested:u(1) = [0.02, 0.04, 0.06, 0.08, 0.10, 0.20, 0.30, 0.40, 0.5] and ur = [0.20, 0.40, 0.60, 0.8]. The minimum (u(1), ur) works for all our worms was (0.08,0.20) • In practice, we use conservative value (0.15,0.5)