Efficient Deep Packet Inspection with Parallel Bloom Filters

1. Deep Packet Inspection Using Parallel Bloom Filters Sarang Dharmapurikar, Praveen Krishnamurthy, Tood Sproull, John Lockwood Presented by Jamie Huenefeld

2. Outline • Motivation and Introduction • Related Works • Bloom Filter Theory • System Overview • Hardware Design Considerations • Implementation and Results • Conclusions

3. Motivation and Introduction • Desire high throughput Intrusion Detection Systems • Fast Attack Signature Matching

4. Motivation and Introduction • Bloom Filter • Data structure that represents a set of strings and can verify set membership • Fast and efficient storage mechanism • Query time is independent of database size, if storage memory scales linearly • No false negatives

5. Related Works • Software • Use of Aho-Corasick Boyer-Moore algorithm for Snort performance • Set-wise implementation of ACBM • Hardware • Use of reconfigurable hardware • Resource consumption grows linearly with search string size • Time consuming updates

6. Bloom Filter Theory • Adding Strings • String x • k hash functions • k hashed values, 1 to m • m bit vector • Set k bits in vector, according to hashed values

7. Bloom Filter Theory • Add “apple” and “plums” • m = 12 • k = 3 • apple hashed values: 3, 11, 12 • plums hashed values: 1, 8, 11

8. Bloom Filter Theory • Check for “mangos” • Mangos hashed values: 3, 8, 12 • String verifies as a false positive

9. Bloom Filter Theory • False Positive Rate • f = (1 - e -nk/m)k • Minimized False Positive Probability • k = (m/n) ln 2 • Increasing k reduces FPP • False Positive Probability Ratio • f = (1/2)k

10. Bloom Filter Theory • Counting Bloom Filters • Incorporates string deletion from the filter • Uses a vector of counting vectors at each bit • Increment count when adding, decrement when deleting 1 0 1 0 0 0 0 1 0 0 1 1

11. System Overview Strings are probed by Analyzer

12. System Overview

13. System Overview

14. System Description • Throughput • R = 1/(Tadd + Tend ) bytes/sec • Throughput with shared Analyzer • f=(1/2)((m/g)/n)ln 2

15. System Description • Hash tables have constant search time • String retrieval from memory depends on string length, however. • 4 byte data bus with = 8 clock cycles • RG = G/(GTadd + Tend) bytes/sec • Less memory + increasing false positives = lower throughput

16. System Description

17. Hardware Design

18. Hardware Design • Multiport Embedded Memory • Hash functions map to m bit memory array • Smaller chunks of memory creates more capacity • Restrict range of hash lookup to bypass memory retrieval constraints.

19. Implementation and Results • Xilinx XCV2000E • Signatures = 32 bytes • 160 block memories • 4096 bit array • 2 reads/clock • M=4096 • K = 2 • 1434 signatures • Using 7 parallel filters • 10038 signatures

20. Implementation and Results

21. Conclusions • Constant Time Queries • Fast updates • Trade-off between memory resources and system performance • Good for real-time processing systems