RAIDM: Router-based Anomaly/Intrusion Detection and Mitigation

1. RAIDM: Router-based Anomaly/Intrusion Detection and Mitigation Zhichun Li EECS Deparment Northwestern University 2008-04-29 Thesis Proposal

2. Outline Motivation RAIDM System Design Finished Work Proposed Work Research Plan

3. Motivation Attackers Botnets Worms

4. Motivation • Network security has been recognized as the single most important attribute of their networks, according to survey to 395 senior executives conducted by AT&T. • Many new emerging threats make the situation even worse. RAIDM Network-based attack defense system

5. Network Level Defense • Network gateways/routers are the vantage points for detecting large scale attacks • Only host based detection/prevention is not enough for modern enterprise networks. • Enterprises might not only want to reply on their end user for security protection • User might not want to stop their work to reboot machines or applications for applying patches.

6. Outline Motivation RAIDM System Design Finished Work Proposed Work Research Plan

7. Research Questions • How can we achieve online anomaly detection for high-speed networks? • How can we respond to zero-day polymorphic worms in their early stage? • Given vulnerabilities, how to protect the high-speed networks from exploits, accurately and efficiently? • How can we provide quality information for network situational awareness?

8. System Framework

9. Current Status • Part I: Sketch based monitoring & detection • Result in [Infocom06,ToN,ICDCS06] • Part II: Polymorphic worm signature generation • Result in [Oakland06,ICNP07] • Part III: Signature matching engines • Work in progress, will be focus of this talk • Part IV: Network Situational Awareness • Work in process

10. Outline Motivation RAIDM System Design Finished Work Proposed Work Research Plan

11. Part I: Sketch based monitoring & detection • Reversible Sketches (include for completeness) • Use intelligent hash function design to recover the aggregated value of a series (key,value) updates for the popular keys. • Publications: • Robert Schweller, Zhichun Li, Yan Chen, Yan Gao, Ashish Gupta, Elliot Parons, Yin Zhang, Peter Dinda, Ming-Yang Kao, and Gokhan Memik, Reversible sketches: Enabling monitoring and analysis over high speed data streams, in the IEEE/ACM Transaction on Networking, Volume 15, Issue 5, Oct, 2007 • Robert Schweller, Zhichun Li, Yan Chen, Yan Gao, Ashish Gupta, Elliot Parons, Yin Zhang, Peter Dinda, Ming-Yang Kao, and Gokhan Memik, Reverse Hashing for High-speed Network Monitoring: Algorithms, Evaluations, and Applications, in the Proc. Of IEEE INFOCOM 2006 (252/1400=18%)

12. Part I: Sketch based monitoring & detection • Sketch-based Anomaly Detection • Build anomaly detection engines based on reversible sketches to detect horizontal scan, vertical scan, and TCP SYN flooding attacks. • Further proposed 2D sketches to differentiate the different types of attacks. • Publications • Yan Gao, Zhichun Li and Yan Chen, A DoS Resilient Flow-level Intrusion Detection Approach for High-speed Networks, In Proc. Of IEEE International Conference on Distributed Computing Systems (ICDCS) 2006 (75/536=14%) (Alphabetical order)

13. Part II: Polymorphic worm signature generation • TOSG (Token-Based Signature Generation) • Use token (substring) conjunction as the signature for polymorphic worms • Advantage • Do not require protocol knowledge or the information about the vulnerable program • Fast and noise tolerant • Have analytical attack resilience bound under certain assumptions. • Limitation • Do not have good attack resilience to the deliberate noise injection attack [Perdisci 2006] • PublicationZhichun Li, Manan Sanghi, Brian Chavez, Yan Chen and Ming-Yang Kao, Hamsa: Fast Signature Generation for Zero-day Polymorphic Worms with Provable Attack Resilience, in Proc. of IEEE Symposium on Security and Privacy, 2006 (23/251=9%)

14. Part II: Polymorphic worm signature generation • LESG (Length-Based Signature Generation) • Propose to use a set of field lengths of the protocol of vulnerable program as signatures. • Mainly work for buffer overflow worms • Advantage: • Fast and noise tolerant • Have analytical attack resilience bound under certain assumptions • The bound hold under all the recently proposed attacks. • PublicationZhichun Li, Lanjia Wang, Yan Chen and Zhi (Judy) Fu, Network-based and Attack-resilient Length Signature Generation for Zero-day Polymorohic Worms, in the Proc. of IEEE International Conference on Network Protocols (ICNP) 2007 (32/220=14%)

15. Outline Motivation RAIDM System Design Finished Work Proposed Work Research Plan

16. Proposed Work • Part III: Signature Matching Engine • NetShield, a protocol semantic vulnerability signature matching engine. (focus on this talk) • ReportZhichun Li, Gao Xia, Yi Tang, Ying He, Yan Chen and Bin Liu, NetShield : Towards High Performance Network-based Semantic Signature Matching

17. Proposed Work • Part IV: Network Situational Awareness • Botnet Inference: • Infer scan properties based on honeynet traffic: trend, uniform, hitlist, and collaboration • Extrapolate the global scan scope and global number of bots based on limited local observation. Can be used to detect target attacks. • ReportZhichun Li, Anup Goyal, Yan Chen and Vern Paxson, Towards Situational Awareness of Large-Scale Botnet Events using Honeynets • P2P Misconfiguration Diagnosis • Found P2P misconfiguration traffic is one of the major source of Internet background radiation • eMule P2P misconfiguration is due to byte ordering • For BitTorrent, we found anti-P2P company deliberately inject bogus peers • ReportZhichun Li, Anup Goyal, Yan Chen and Aleksandar Kuzmanovic, P2P Doctor: Measurement and Diagnosis of Misconfigured Peer-to-Peer Traffic

18. NetShield Overview • Goal • Feasibility Study: a Measurement Approach • High Speed Parsing • High Speed Matching for Large Rulesets • Preliminary Evaluation • Discussion

19. Signature Matching Engine • Accuracy (especially for IPS) • False positive • False negative • Speed • Coverage: Large ruleset

20. Reason Regular expression is not power enough to capture the exact vulnerability condition! Shield RE X Cannot express exact condition Can express exact condition

21. Feasibility Study • Protocol semantic can help (Shield project [SIGCOMM04]) • How much for NIDS/IPS? • Given a NIDS/NIPS has a large ruleset • What percent of the rules can use protocol semantic vulnerability signature to improve?

22. Measure Snort rules • Semi-manually classify the rules. • First by CVEID • Manually look at each vulnerability • Results • 86.7% of rules can be improved by protocol semantic vulnerability signatures. • 9.9% of rules are web DHTML and scripts related which are not suitable for signature based approach. • On average 4.5 Snort rules reduce to one vulnerability signature • Binary protocols have large reduction ratio than text based protocols.

23. Towards high speed parsing • Protocol parsing problem formulation • Given a PDU and the previous states from previous PDU, output the set of fields which required by matching. • Observation • Parsing State Machine

24. PDU array Observation • PDU  parse tree • Leaf nodes (basic fields ) are integer or string • Vulnerability signature mostly based on basic fields Only need to parse out the field related to signatures

25. Parsing State Machine • Studied eight popular protocols: HTTP, FTP, SMTP, eMule, BitTorrent, WINRPC, SNMP and DNS and vulnerability signatures. • Protocol semantics are context sensitive • Common relationship among basic fields.

26. Example for WINRPC • Nodes • States: S1 .. Sn • 0.61 instruction/byte for BIND PDU

27. High speed matching • Problem formulation • Observation • Candidate Selection Algorithm • Algorithm Refinement

28. Matching Problem Formulation • Data presentation • For all the vulnerability signartures we studied we need integers and strings • Integer operator: ==, >, < • String operator: ==, match_re(.,.), len(.), • Buffer constraint • The string fields could be too long to buffer. • Influence whether we can change the matching order • Field dependency • Array (e.g., DNS_questions, or RR records) • Associate array (e.g., HTTP headers) • Mutual exclusive fields.

29. Matching Problem Formulation (2) • PDU level protocol state machine • For complex stateful protocols • For most stateful protocols the state machine is quite simple WINRPC example

30. Matching problems (cont.) • Example signature for Blaster worm • Single PDU matching problem (SPM) • Multiple PDU matching problem (MPM)

31. Single PDU Matching • Suppose we have n signatures, each is defined on k matching dimensions (matchers) • Matcher is a two tuple (field, operation) or four tuple for the associate array elements. • For example: • (Filename, RE) • (Version, Range_check) • Version > 3 • Version == 1 • k is all possible matchers for the n signatures.

32. Table Representation • We use a n×k table to represent the rules. k matchers n row signatures

33. Requirement for SPM • Large number of signatures n • Large number of matchers k • Large number of “don’t cares” • Cannot reorder the matchers arbitrarily (buffer constraint) • Field dependency • Array • Associate Array • Mutually exclusive Fields.

34. Compare to packet classification • Similarity: both problem define on k matching dimensions and allow wildcards • Differences: • Large k and large number of “don’t cares” • Buffer constraint • Regular expression matcher • Field dependency • Related work on packet classification • Exhaustive search • Decision tree • Tuple space • Divide and Conquer (Decomposition)

35. Difficulty • A more complex problem than packet classification • Packet classification theoretical worst case bound • Based on computational geometry • O ((logN)k-1) worst case time or O (Nk) worst case memory • Solution: use the characteristics from real traces

36. Observation • Observation 1: most matchers are good. • After matching against them, only a small number of signatures can pass (candidates). • String matchers are all good, most integer matchers are good. • We can buffer the bad matchers to change the matching order • Observation 2: real world traffic mostly does not match any signature. Actually even stronger in most case no matcher will match any rule. • Observation 3: the NIDS/IPS will report all the matched rules regardless the ordering. Differ from firewall rules.

37. Basic idea • Decide the matcher order at pre-computation, buffer the bad ones to the end if possible • When a PDU comes, match again each matcher (column) for all the signatures simultaneously and get the possible candidates for next step • Combine the candidate sets together to get the final matched signatures

38. Match single matcher • Integer range checking: Binary search tree • String exact matching: Trie • String regular expression matching: DFA. • String length checking: Binary search tree

39. Candidate Selection for SPM • Basic algorithm: pre-computation

40. Matching Illustration A2 candidates B2 candidates

41. Matching Illustration • Compute the operations • Explicit calculation • Based on a n×k Bitmap decide the whether an element in Si requires next matchers. • For those requires next matchers, search whether it is also in Ai+1 • Implicit calculation (for bad matchers) • Do not calculate Ai+1 , since it could be large • Check whether the candidates in Si can match matcher (i+1) sequentially • When buffer bad matchers to the end, the B will be small.

42. Refinement • SPM improvement • Allow negative conditions • Handle array case • Handle associate array case • Handle mutual exclusive case • Report the matched rules as early as possible • Extend to MPM • Allowing checkpoints.

43. Results • Traces from Tsinghua Univ. (TH) and Northwestern Univ. (NU) • After TCP reassembly and preload the PDU in memory • For DNS we only evaluate parsing. • For WINRPC we have 45 vulnerability signatures which covers 3,519 Snort rules • For HTTP we have 791 vulnerability signatures which covers 941 Snort rules.

44. Discussion • Currently we found the candidate selection algorithm works well in practice • Further thoughts • How to rely more on hardware assistance? • TCAM? • Use bitmap to express set operations? • Whether we can consider the traffic statistics to further improve efficiency?

45. Outline Motivation RAIDM System Design Finished Work Proposed Work Research Plan

46. Publications • Zhichun Li, Lanjia Wang, Yan Chen and Zhi (Judy) Fu, Network-based and Attack-resilient Length Signature Generation for Zero-day Polymorohic Worms, in the Proc. of IEEE ICNP 2007. • Robert Schweller, Zhichun Li, Yan Chen, Yan Gao, Ashish Gupta, Elliot Parons, Yin Zhang, Peter Dinda, Ming-Yang Kao, and Gokhan Memik, Reversible sketches: Enabling monitoring and analysis over high speed data streams, in the IEEE/ACM Transaction on Networking, Volume 15, Issue 5, Oct, 2007 • Zhichun Li, Manan Sanghi, Brian Chavez, Yan Chen and Ming-Yang Kao, Hamsa: Fast Signature Generation for Zero-day Polymorphic Worms with Provable Attack Resilience, in Proc. of IEEE Symposium on Security and Privacy, 2006 • Zhichun Li, Yan Chen and Aaron Beach, Towards Scalable and Robust Distributed Intrusion Alert Fusion with Good Load Balacing, in Proc. of ACM SIGCOMM LSAD 2006 • Yan Gao, Zhichun Li and Yan Chen, A DoS Resilient Flow-level Intrusion Detection Approach for High-speed Networks, In Proc. Of IEEE ICDCS 2006 • Robert Schweller, Zhichun Li, Yan Chen, Yan Gao, Ashish Gupta, Elliot Parons, Yin Zhang, Peter Dinda, Ming-Yang Kao, and Gokhan Memik, Reverse Hashing for High-speed Network Monitoring: Algorithms, Evaluations, and Applications, in the Proc. Of IEEE INFOCOM 2006

47. Research Time Plan • Apr 2008 – Jun 2008: • Finish remaining experiments of network situational awareness • Sep 2008 – Mar 2008: • Refine the vulnerability signature matching algorithm • Fully implement, deploy and evaluate the Netshield prototype • Prepare job application and interview • Apr 2009 – Jun 2009: • PhD dissertation writing • Thesis Defense

48. Q & A Thanks!

49. Backup

50. Outline Motivation Feasibility Study: a measurement approach Problem Statement High Speed Parsing High Speed Matching for massive vulnerability Signatures. Evaluation Conclusions