1. Yan Chen Lab for Internet and Security Technology EECS Department Northwestern University Intrusion Detection and Forensics for Self-defending Wireless Networks

2. Security Challenges in GIG Wireless Networks In addition to sharing similar challenge of wired net –High speed traffic (e.g., WiMAX) –Zero-day threats –Lack of quality info for situational-aware analysis: attack target/strategy, attacker (botnet) size, etc. Wireless networks are more vulnerable –Many emerging wireless network protocols: WiMAX, mobile IP v4/6, EAP authentication protocols

3. Self-Defending Wireless Networks Proactive vulnerability analysis of wireless network protocols (done in year 1) –Found a class of exception triggered DoS attacks Net-based adaptive anomaly diagnosis, intrusion detection and mitigation –Polymorphic zero-day worm signature generation (done in year 2) –Automated analysis of large-scale botnet probing events for situation aware info (done in year 3) –Scalable signature matching w/ massive vulnerability signatures (done in year 4) Projects done in pipeline, not serialized.

4. Five conference papers Detecting Stealthy Spreaders Using Online Outdegree Histograms, in the Proc. of the 15th IEEE International Workshop on Quality of Service (IWQoS), 2007 (26.6%). Hamsa: Fast Signature Generation for Zero-day Polymorphic Worms with Provable Attack Resilience, to appear in IEEE Symposium on Security and Privacy, 2006 (9%). Towards Scalable and Robust Distributed Intrusion Alert Fusion with Good Load Balancing, in Proc. of ACM SIGCOMM Workshop on Large-Scale Attack Defense 2006(33%). Automatic Vulnerability Checking of IEEE 802.16 WiMAX Protocols through TLA+, in Proc. of the Second Workshop on Secure Network Protocols (NPSec) (33%). A DoS Resilient Flow-level Intrusion Detection Approach for High-speed Networks, in IEEE International Conference on Distributed Computing Systems (ICDCS), 2006 (14%). Accomplishments in AY 06-07

5. Accomplishments in AY 07-08 Three conference, one journal papers and two book chapters, and one patent filed “Accurate and Efficient Traffic Monitoring Using Adaptive Non-linear Sampling Method", in the Proc. of IEEE INFOCOM, 2008 “A Survey of Existing Botnet Defenses “, in Proc. of IEEE IWSSE 2008. “Honeynet-based Botnet Scan Traffic Analysis", invited book chapter for “Botnet Detection: Countering the Largest Security Threat”, Springer, 2007. “Integrated Fault and Security Management”, invited book chapter for “Information Assurance: Dependability and Security in Networked Systems”, Morgan Kaufmann Publishers, 2007. “Reversible Sketches: Enabling Monitoring and Analysis over High-speed Data Streams”, in ACM/IEEE Transaction on Networking, Volume 15, Issue 5, Oct. 2007. “Network-based and Attack-resilient Length Signature Generation for Zero-day Polymorphic Worms”, in the Proc. of the IEEE ICNP, 2007. Collaborated publication with Dr. Keesook Han from AFRL

6. Accomplishments in AY 08-09 Five conference and one journal papers “Using Failure Information Analysis to Detect Enterprise Zombies", in the Proc. of SecureComm 2009. “Exception Triggered DoS Attacks on Wireless Networks”, the 39th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2009. "BotGraph: Large Scale Spamming Botnet Detection", USENIX Symposium on Networked Systems Design and Implementation (NSDI) 2009. "Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints", IEEE INFOCOM (main conference), 2009. “Automating Analysis of Large-Scale Botnet Probing Events”, ACM Symposium on Information, Computer and Communications Security (ASIACCS), 2009. “Pollution Attacks and Defenses for Internet Caching Systems”, in Journal of Computer Networks, 2008.

7. Accomplishments in AY 09-10 Two conference and four journal papers and one patent filed “NetShield: Massive Semantics-based Vulnerability Signature Matching for High-speed Networks, in the Proc. of ACM SIGCOMM 2010 HiFIND: A high-speed flow-level intrusion detection approach with DoS resiliency, Journal of Computer Networks, Volume 54, Issue 8, June 2010.. Measurement and Diagnosis of Address Misconfigured P2P Traffic, in the Proc. of IEEE INFOCOM, 2010 Thwarting Zero-Day Polymorphic Worms With Network-Level Length-Based Signature Generation, in ACM/IEEE Transaction on Networking (ToN), Volume 18, Issue 1, 2010. POPI: A User-level Tool for Inferring Router Packet Forwarding Priority", ACM/IEEE Transaction on Networking (ToN), Volume 18, Issue 1, 2010. "Towards Unbiased End-to-End Network Diagnosis", in ACM/IEEE Transaction on Networking (ToN), Volume 17, Number 6, Dec. 2009.

8. 88 15 conference papers 6 journal papers 2 book chapters 2 patents filed Several software released to the community –E.g. www.nshield.orgwww.nshield.org Invited talks to Cisco, Juniper, etc. for potential tech transfer Overall Achievement

9. 9 NetShield: Matching a Large Vulnerability Signature Ruleset for High Performance Network Defense

10. 10 Outline Motivation High Speed Matching for Large Rulesets High Speed Parsing Evaluation Research Contributions

11. 11 NetShield Overview NIDS/NIPS (Network Intrusion Detection/Prevention System) operation Signature DB NIDS/NIPS Packets Security alerts Accuracy Speed Attack Coverage

12. 12 Network Level Defense Network gateways/routers are the vantage points for detecting large scale attacks Only host based detection/prevention is not enough –Some users do not apply the host-based schemes due to the reliability, overhead, and conflicts –Many users do not update or patch their system on time –E.g., Conficker worm in the end of 2008 infected 9~15 millions of hosts –Cannot only reply on end users for security protection

13. 13 State Of The Art Pros Can efficiently match multiple sigs simultaneously, through DFA Can describe the syntactic context Regular expression (regex) based approaches Used by: Cisco IPS, Juniper IPS, open source Bro Example:.*Abc.*\x90+de[^\r\n]{30}

14. Cons of Regex Regex Context Free Context Sensitive Protocol grammar Theoretical prospective Practical prospective HTTP chunk encoding DNS label pointers Limited expressive power, cannot describe semantic context, thus inaccurate

15. 15 State Of The Art Pros Directly describe semantic context Very expressive, can express the vulnerability condition exactly Accurate Vulnerability Signature [Wang et al. 04] Cons Slow! Existing approaches all use sequential matching Require protocol parsing Blaster Worm (WINRPC) Example: BIND: rpc_vers==5 && rpc_vers_minor==1 && packed_drep==\x10\x00\x00\x00 && context[0].abstract_syntax.uuid=UUID_RemoteActivation BIND-ACK: rpc_vers==5 && rpc_vers_minor==1 CALL: rpc_vers==5 && rpc_vers_minors==1 && packed_drep==\x10\x00\x00\x00 && opnum==0x00 && stub.RemoteActivationBody.actual_length>=40 && matchRE(stub.buffer, /^\x5c\x00\x5c\x00/) Good state Bad state Vulnerability Signature Vulnerability: design flaws enable the bad inputs lead the program to a bad state Bad input

16. 16 Motivation of NetShield 16

17. 17 Motivation Desired Features for Signature-based NIDS/NIPS –Accuracy (especially for IPS) –Speed –Coverage: Large ruleset Regular Expression Vulnerability AccuracyRelative Poor Much Better SpeedGood?? MemoryOK?? CoverageGood?? Shield [sigcomm’04] Focus of this work Cannot capture vulnerability condition well!

18. 18 Research Challenges and Solutions Challenges –Matching thousands of vulnerability signatures simultaneously Sequential matching  match multiple sigs. simultaneously –High speed protocol parsing Solutions –An efficient algorithm which matches multiple sigs simultaneously –A tailored parsing design for high-speed signature matching

19. 19 Background Vulnerability signature basic –Use protocol semantics to express vulnerabilities –Defined on a sequence of PDUs & one predicate for each PDU –Example: ver==1 && method==“put” && len(buf)>300 Data representations –For all the vulnerability signatures we studied, we only need numbers and strings –number operators: ==, >, =, <= –String operators: ==, match_re(.,.), len(.). Blaster Worm (WINRPC) Example: BIND: rpc_vers==5 && rpc_vers_minor==1 && packed_drep==\x10\x00\x00\x00 && context[0].abstract_syntax.uuid=UUID_RemoteActivation BIND-ACK: rpc_vers==5 && rpc_vers_minor==1 CALL: rpc_vers==5 && rpc_vers_minors==1 && packed_drep==\x10\x00\x00\x00 && opnum==0x00 && stub.RemoteActivationBody.actual_length>=40 && matchRE(stub.buffer, /^\x5c\x00\x5c\x00/)

20. 20 Outline Motivation High Speed Matching for Large Rulesets High Speed Parsing Evaluation Research Contributions

21. 21 Matching Problem Formulation Suppose we have n signatures, defined on k matching dimensions (matchers) –A matcher is a two-tuple (field, operation) or a four- tuple for the associative array elements –Translate the n signatures to a n by k table –This translation unlocks the potential of matching multiple signatures simultaneously Rule 4: URI.Filename=“fp40reg.dll” && len(Headers[“host”])>300 RuleIDMethod ==Filename ==Header == LEN 1DELETE** 2POSTHeader.php* 3*awstats.pl* 4*fp40reg.dllname==“host”; len(value)>300 5**name==“User-Agent”; len(value)>544

22. 22 Matching Problem Formulation Challenges for Single PDU matching problem (SPM) –Large number of signatures n –Large number of matchers k –Large number of “don’t cares” –Cannot reorder matchers arbitrarily -- buffering constraint –Field dependency Arrays, associative arrays Mutually exclusive fields.

23. 23 Difficulty of the SPM Bad News –A well-known computational geometric problem can be reduced to this problem. –And that problem has bad worst case bound O((log N) K-1 ) time or O(N K ) space (worst case ruleset) Good News –Measurement study on Snort and Cisco ruleset –The real-world rulesets are good: the matchers are selective. –With our design O(K)

24. 24 Matching Algorithms Candidate Selection Algorithm 1.Pre-computation decides the rule order and matcher order 2.Decomposition. Match each matcher separately and iteratively combine the results efficiently Integer range checking  balanced binary search tree String exact matching  Trie Regex  DFA (XFA)

25. 25 Outline Motivation High Speed Matching for Large Rulesets. High Speed Parsing Evaluation Research Contribution

26. High Speed Parsing Design a parsing state machine Build an automated parsing state machine generator General V.S. Special Purpose Keep the whole parse tree in memory Parsing and matching on the fly Parse all the nodes in the tree Only signature related fields (leaf nodes) V.S.

27. 27 Outline Motivation High Speed Matching for Large Rulesets. High Speed Parsing Evaluation Research Contributions

28. 28 Evaluation Methodology 26GB+ Traces from Tsinghua Univ. (TH), Northwestern (NU) and DARPA Run on a P4 3.8Ghz single core PC w/ 4GB memory After TCP reassembly and preload the PDUs in memory For HTTP we have 794 vulnerability signatures which cover 973 Snort rules. For WINRPC we have 45 vulnerability signatures which cover 3,519 Snort rules 28 Fully implemented prototype 12,000 lines of C++ and 3,000 lines of Python Release at: www.nshield.org Deployed at a university DC with up to 106Mbps

29. 29 Parsing Results Trace TH DNS TH WINRPC NU WINRPC TH HTTP NU HTTP DARPA HTTP Avg flow len (B) 778795966.6K55K2.1K Throughput (Gbps) Binpac Our parser 0.31 3.43 1.41 16.2 1.11 12.9 2.10 7.46 14.2 44.4 1.69 6.67 Speed up ratio 11.211.511.63.63.13.9 Max. memory per connection (bytes) 15 14

30. 30 Matching Results TraceTH WINRPC NU WINRPC TH HTTP NU HTTP DARPA HTTP Avg flow length (B) 8795966.6K55K2.1K Throughput (Gbps) Sequential CS Matching 10.68 14.37 9.23 10.61 0.34 2.63 2.37 17.63 0.28 1.85 Matching only time speed up ratio 41.811.311.78.8 Avg # of Candidates 1.161.480.0330.0380.0023 Max. memory per connection (bytes) 27 20 11.0 8-core

31. 31 Scalability and Accuracy Results Create two polymorphic WINRPC exploits which bypass the original Snort rules but detect accurately by our scheme. For 10-minute “clean” HTTP trace, Snort reported 42 alerts, NetShield reported 0 alerts. Manually verify the 42 alerts are false positives Rule scaling results Performance decrease gracefully Accuracy

32. 32 Research Contribution Regular ExpressionExists Vul. IDSNetShield AccuracyPoorGood SpeedGoodPoorGood MemoryGood??Good CoverageGood??Good Build a better Snort alternative! Multiple sig. matching  candidate selection algorithm Parsing  parsing state machine Make vulnerability signature a practical solution for NIDS/NIPS

33. 33 Q & A Thanks!

34. 34 Observations array PDU PDU  parse tree Leaf nodes are numbers or strings General V.S. Special Purpose Keep the whole parse tree in memory Parsing and matching on the fly Parse all the nodes in the tree Only signature related fields (leaf nodes) V.S.

35. 35 Efficient Parsing with State Machines Studied eight protocols: HTTP, FTP, SMTP, eMule, BitTorrent, WINRPC, SNMP and DNS as well as their vulnerability signatures Common relationships among leaf nodes Pre-construct parsing state machines based on parse trees and vulnerability signatures Automated parsing state machine generator: UltraPAC

36. 36 Example for WINRPC Rectangles are states Parsing variables: R 0.. R 4 0.61 instruction/byte for BIND PDU

37. 37 Step 1: Pre-Computation Optimize the matcher order based on buffering constraint & field arrival order Rule reorder : Require Matcher 1 Don’t care Matcher 1 Require Matcher 1 Require Matcher 2 Don’t care Matcher 1 & 2 1 n

38. 38 Step 2: Iterative Matching RuleIDMethod ==Filename ==Header == LEN 1DELETE** 2POSTHeader.php* 3*awstats.pl* 4*fp40reg.dllname==“host”; len(value)>300 5**name==“User-Agent”; len(value)>544 PDU={Method=POST, Filename=fp40reg.dll, Header: name=“host”, len(value)=450} S 1 ={2} Candidates after match Column 1 (method==) S2=S2=S1S1 A2A2 +B2+B2 ={2}{}+{4}={}+{4}={4} S 3 =S 2 A3+B3A3+B3 ={4}{4}+{}={4}+{}={4} Si Don’t care matcher i+1 require matcher i+1 In A i+1 R1 R2 R3

39. 39 Complexity Analysis Merging complexity –Need k -1 merging iterations –For each iteration Merge complexity O(n) the worst case, since S i can have O(n) candidates in the worst case rulesets For real-world rulesets, # of candidates is a small constant. Therefore, O(1) –For real-world rulesets: O(k) which is the optimal we can get Three HTTP traces: avg(|S i |)<0.04 Two WINRPC traces: avg(|S i |)<1.5

40. 40 Refinement and Extension SPM improvement –Allow negative conditions –Handle array cases –Handle associative array cases –Handle mutual exclusive cases Extend to Multiple PDU Matching (MPM) –Allow checkpoints.

41. 41 Experiences Working in process –In collaboration with MSR, apply the semantic rich analysis for cloud Web service profiling. To understand why slow and how to improve. Interdisciplinary research Student mentoring (three undergraduates, six junior graduates)

42. 42 Future Work Near term –Web security (browser security, web server security) –Data center security –High speed network intrusion prevention system with hardware support Long term research interests –Combating professional profit-driven attackers will be a continuous arm race –Online applications (including Web 2.0 applications) become more complex and vulnerable. –Network speed keeps increasing, which demands highly scalable approaches.

43. 43 Research Contributions Demonstrate vulnerability signatures can be applied to NIDS/NIPS, which can significantly improve the accuracy of current NIDS/NIPS Propose the candidate selection algorithm for matching a large number of vulnerability signatures efficiently Propose parsing state machine for fast protocol parsing Implement the NetShield

44. 44 Comparing With Regex Memory for 973 Snort rules: DFA 5.29GB (XFA 863 rules1.08MB), NetShield 2.3MB Per flow memory: XFA 36 bytes, NetShield 20 bytes. Throughput: XFA 756Mbps, NetShield 1.9+Gbps (*XFA [SIGCOMM08][Oakland08])

45. 45 Measure Snort Rules Semi-manually classify the rules. 1.Group by CVE-ID 2.Manually look at each vulnerability Results –86.7% of rules can be improved by protocol semantic vulnerability signatures. –Most of remaining rules (9.9%) are web DHTML and scripts related which are not suitable for signature based approach. –On average 4.5 Snort rules are reduced to one vulnerability signature. –For binary protocol the reduction ratio is much higher than that of text based ones. For netbios.rules the ratio is 67.6.

46. 46 Matcher order Reduce S i+1 Enlarge S i+1 fixed, put the matcher later, reduce B i+1 Merging Overhead | S i | (use hash table to calculate in A i+1, O(1))

47. 47 Matcher order optimization Worth buffering only if estmaxB(M j )<=MaxB For M i in AllMatchers –Try to clear all the M j in the buffer which estmaxB(M j )<=MaxB –Buffer M i if ( estmaxB(M i )>MaxB ) –When len(Buf)>Buflen, remove the M j with minimum estmaxB(M j )