View
215
Download
0
Tags:
Embed Size (px)
Citation preview
1
Fast and Memory-Efficient Regular Expression
Matching for Deep Packet Inspection
Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C.
Authors: Fang Yu, Zhifeng Chen, Yanlei Diao, T.V. Lakshman and Randy H. Katz
Publisher: ANCS'06, December 3–5, 2006
Present: Yu-Tso Chen
Date: November, 6, 2007
2
Outline
1. Introduction 2. Definitions and problem description 3. Matching of Individual Patterns 4. Selective Grouping of Multiple
Patterns 5. Evaluation Result 6. Conclusion
3
Introduction
Three unique complex features• 1) Large numbers of wildcards can cause DFA to
grow exponentially
• 2) Wildcard are used with length restriction(‘?’, ‘+’) will increase the resource
• 3) Groups of characters are also commonly used such interaction can result in highly complex state machine(ex.”^220[\x09-]*ftp”)
4
Introduction (cont.)
Make following contributions• 1) Analyze the computational and storage cost of
building individual DFAs
• 2) Two rewrite rules for specific regular expressions
• 3) Combine multiple DFAs into a small number of group
5
Outline
1. Introduction 2. Definitions and problem
description 3. Matching of Individual Patterns 4. Selective Grouping of Multiple
Patterns 5. Evaluation Result 6. Conclusion
6
Regular Expression Patterns
Compares the regular expressions used in two networking applications (Snort, Linux L-7 filter & XML filtering)• 1)Both types of app. Use wildcards
(‘.’,’?‘,’+’,’*’) contain larger numbers of them
• 2) Classes of characters (“[ ]”) are used only in packet scanning applications
• 3) High percentage of scanning app. Have length restrictions on some of the classes or wildcards
7
Regular Expression Patterns
8
Solution Space for Regular Expression Matching
A single regular expression of length n can be expressed as an NFA with O(n)
When the NFA is converted into a DFA, it may generate states
The processing complexity for each character in the input is O(1) in DFA, but is O(n2) for an NFA when all n states are active at the same time
nO
9
Solution Space for Regular Expression Matching (cont.)
To handle m regular expressions, two choices are possible :• Processing them individually in m automata
• Compiling them into a single automaton
10
Problem Statement
DFA-based approaches in this paper• Our goal is to achieve O(1) computation cost
• The focus of the study is to reduce memory overhead of DFA
There are two sources of memory usage in DFAs : states and transitions• We consider the number of states as the prim
ary factor
11
Outline
1. Introduction 2. Definitions and problem description 3. Matching of Individual Patterns 4. Selective Grouping of Multiple
Patterns 5. Evaluation Result 6. Conclusion
12
Design Considerations
Define Completeness of Matching Results :• Exhaustive Matching:M(P,S)={substring S’ of S | S
’ is accepted by the DFA of P}
• It is expensive and often unnecessary to report all matching substrings
• We propose a new concept, Non-overlapping Matching, that relaxes the requirements of exhaustive matching
• Non-overlapping Matching:
• Ex : ab* if input abbb non-overlapping matching will report one match instead of three
• Exhaustive Matching will report, ab, abb, abbb
}P, ofDFA by the accepted ,|S of Si substring{),( SjSiSjSiSPM
13
Design Considerations (cont.)
Define DFA Execution Model for Substring Matching : We focus on patterns without ‘^’ attached at the beginning• Repeater searches
• One-pass search – this approach can truly achieve O(1) computation cost per character
14
DFA Analysis for Individual Regular Expressions
The study is based on the use of exhaustive matching & one-pass search
15
Case 4 : DFA of Quadratic Size The DFA needs to remember the
number of Bs it has seen and their locations
16
Case 5 : DFA of Exponential Size An exponential number of states
(22+1)are needed to represent these two wildcard characters
AAB(AABBCD) is different from ABA(ABABCD) because a subsequence input BCD
17
Regular Expression Rewrites
Rewrite Rule(1)• “^SEARCH\s+[^\n]{1024}” to
“^SEARCH\s [^\n]{1024}”
• “^A+[A-Z]{j}” to “^A [A-Z]{j}” • We can prove match “^A+[A-Z]{j}” also match “^A
[A-Z]{j}”
18
Regular Expression Rewrites (cont.)
Rewrite Rule(2)• We don’t need to keep track of the second AUTH\s
• If there is a ‘\n’ within the next 100 bytes, the return character must also be within 100 bytes to the second AUTH\s
• If there is no ‘\n’ within the next 100 bytes, the first already matched the pattern
• “([^A]|A[^U]|AU[^T]|AUT[^H]|AUTH[^\s]|AUTH\s[^\n]{0,99}\n)*AUTH\s[^\n]{100}”
19
Outline
1. Introduction 2. Definitions and problem description 3. Matching of Individual Patterns 4. Selective Grouping of Multiple
Patterns 5. Evaluation Result 6. Conclusion
20
Selective Grouping of Multiple Patterns
The composite DFA may experience exponential growth in size, although none of the individual DFA has an exponential component
21
Regular Expressions Grouping Algorithm
Definition of interaction : two patterns interact with each other if their composite DFA contains more states than the sum of two individual ones
22
Grouping Algorithm
23
Outline
1. Introduction 2. Definitions and problem description 3. Matching of Individual Patterns 4. Selective Grouping of Multiple
Patterns 5. Evaluation Result 6. Conclusion
24
Evaluation Result
Effect of Rule Rewriting
25
Evaluation Result (cont.)
26
Outline
1. Introduction 2. Definitions and problem description 3. Matching of Individual Patterns 4. Selective Grouping of Multiple
Patterns 5. Evaluation Result 6. Conclusion
27
Conclusion
Rewriting techniques –
memory-efficient DFA-based approaches are possible
Selectively groups patterns together –
speed up the matching process