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Improving Signature Matching using Binary Decision Diagrams

This study presents an innovative approach to improve signature matching in Intrusion Detection Systems (IDS) using Binary Decision Diagrams. The research focuses on enhancing time and space efficiency for matching network packets against attack signatures. The NFA-OBDD model offers significant speed gains while maintaining low memory consumption compared to traditional NFAs and DFAs. The paper discusses trends, challenges, and the implementation of NFA-OBDD, showcasing substantial performance improvements. Experimental results demonstrate the effectiveness of the proposed method in handling increasing signature set sizes with minimal memory impact.

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Improving Signature Matching using Binary Decision Diagrams

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  1. Improving Signature Matching using Binary Decision Diagrams Liu Yang, Rezwana Karim, Vinod Ganapathy Rutgers University Randy Smith Sandia National Labs

  2. Signature matching in IDS • Find instances of network packets that match attack signatures Sig: /.*evil.*/ 12evil34 signatures innocent

  3. Matching regular expressions • Signatures represented as regular expressions (RE) • Finite Automata • Represent and operate regular expressions • Time Efficiency • Throughput must cope with Gbps link speeds • Space Efficiency • Must fit in main memory of NIDS Time-Space Tradeoff in Finite Automata

  4. Time/Space tradeoff >= 4 orders of magnitude DFAs • DFA : Deterministic Finite Automaton • NFA : Non-Deterministic Finite Automaton Space efficient DFA Space Usage NFA-OBDD NFAs IDEAL Processing time

  5. Contributions of our paper • NFA-OBDD: New data structure that offers fast regular expression matching with space consumption comparable to that of NFAs • Up to 1645x faster than NFAs with comparable memory consumption • Speed is competitive with DFA variants • DFA runs out of memory for our signature sets • Outperforms or is competitive with PCRE Key Idea: Boolean encoding of NFA operation

  6. Trends and challenges Signature set size increased 5x in the last 5 years Challenge Perform fast matching with low memory consumption

  7. Combining DFAs Multiplicative increase in number of states /.*ab.*cd/ /.*ef.*gh/ /.*ab.*cd | .*ef.*gh/ Picture courtesy : [Smith et al. Oakland’08]

  8. Combining NFAs Additive increase in number of states M N /.*ab.*cd/ /.*ef.*gh/ /.*ab.*cd | .*ef.*gh/

  9. NFAs more compact than DFAs NFA DFA Signature /.*1[0|1] {3} / Real Snort signatures have large counter values

  10. DFA Signature 1: /.*ab.*.{15}cd/ Signature 2: /.*ef.*.{10}gh/ NFA

  11. Outline • Problem Definition • Our Contribution • NFA-OBDD model and operation • Implementation • Evaluation • Related Work • Conclusion

  12. NFA-OBDDs: Main idea • Why are NFAs slow? • NFA frontiers may contain multiple states • Each frontier state must be processed for each symbol in the input. • Idea: Represent and operate NFA frontiers symbolically using Boolean functions • Entire frontier can be modified using a single Boolean formula • Use ordered binary decision diagrams (OBDDs) to represent Boolean formulae

  13. Encoding NFA transition functions • An NFA for (0|1)*1, ∑ = {0,1} • Transition function 1 0 1 1 • Frontier: Set of current states • Size: O(n); n= # of states • Set membership function • -Disjunction of binary values of member states 1 0 0 1

  14. NFA operation • Determine new frontier after processing input: Next set of states = • Checking acceptance: Mapy→x ( Ǝx,i Transition_Function(x,i,y) ᴧ Frontier(x) ᴧ Input_Symbol(i) ) SAT(Set_of_ Accept_States(x) ᴧ Frontier(x))

  15. Ordered binary Decision Diagram (OBDD) [Bryant 86] • Compact representation of Boolean function The BDD of f(x,i,y) with order i < x < y

  16. NFA-OBDD • NFA-OBDD: NFA representation and operation using OBDDs • OBDD Representation of • Transitions • Frontiers • Current set of states • Input symbols • Set of accepting states • Set of start states

  17. Space efficiency of NFA-OBDDs • NFA-OBDD construction: • Uses same combination algorithm as NFAs • OBDD data structure itself utilizes the redundancy of the binary function table Rapid growth of signature set has little impact on NFA-OBDD space consumption (unlike DFAs)

  18. Outline • Problem Definition • Our Contribution • NFA-OBDD model and operation • Implementation • Evaluation • Related Work • Conclusion

  19. Experimental apparatus • C++ and CUDD package for OBDDs

  20. Regular expression sets • Snort HTTP signature set • 1503 regular expressions from March 2007 • 2612 regular expressionsfrom October 2009 • Snort FTP signature set • 98 regular expressions from October 2009 • Extracted regular expressions from pcre and uricontent fields of signatures

  21. Traffic traces • HTTP traces • 33 traces • Size: 5.1MB –1.24 GB • One week period in Aug 2009 from Web server of the CS department at Rutgers • FTP Traces • 2 FTP traces • Size: 19.4MB, 24.7 MB • Two week period in March 2010 from FTP server of the CS department at Rutgers

  22. Experimental results • For 1503 REs from HTTP Signatures 10x 1645x 9-26x *Intel Core2 Duo E7500, 2.93GHz; Linux-2.6; 2GB RAM*

  23. Experimental results • For 2612 REs from HTTP signatures MDFA ran out of memory for 2612 REs

  24. Experimental results • For 98 RE from FTP signatures MDFA ran out of memory for 98 REs

  25. Multibyte matching • Matches k>1 input symbol in a single step • Also possible with NFA-OBDDs • Use OBDDs to represent k-step transitive closure of NFA transition function • See paper for details. • Brief summary of experimental results • 2-stride NFA-OBDD doubles the throughput • Outperforms 2-stride NFA by 3 orders of magnitude

  26. Related work • Multiple DFAs [Yu et al., ANCS’06] • Extended finite automata [Smith et al., Oakland’08, SIGCOMM’08] • D2FA [Kumar et al., SIGCOMM’06] • Hybrid finite automata [Becchi et al., ANCS’08] • Multibyte speculative matching [Luchaup et al., RAID’09] • Many more – see paper for details

  27. Conclusion • NFA-OBDDs • Outperform NFAs by three orders of magnitude • Up to 1645× in the best case • Retain space efficiency of NFAs • Outperform or competitive with the PCRE package • Competitive with variants of DFAs but drastically less memory-intensive

  28. Improving Signature Matching using Binary Decision Diagrams Thank You

  29. BDD var ordering

  30. NFA-OBDD vs NFA NFA-OBDD • Time Efficient • Process a set of states in one step • Space Efficient • Use the same combination algorithm

  31. NFA-OBDD operation Temp] = OBDD[Transition Function] ᴧOBDD[Frontier] ᴧOBDD[Input Symbol] OBDD[Next Set of States] = Map x→y (Ǝx,i OBDD[Temp]) Checking Acceptance : OBDD[Set of Accept States] ᴧOBDD[Frontier]

  32. Solution for Mutibyte Matching

  33. Experimental results • 2 Stride 1400 HTTP

  34. Experimental results • 2 Stride 1400 HTTP signatures zoomed

  35. Experimental Results • 2 stride ftp 95 signatures

  36. Matching multiple input symbol

  37. accept sig

  38. accept sig accept sig accept sig accept sig accept sig accept sig accept sig accept sig

  39. NFA is compact Signature /.*1[0|1] {3}/ Recent signatures have large counter with value up to 500

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