Title
Benchmarking of compressed DFAs for traffic identification: Decoupling data structures from models
Abstract
Current network traffic analysis systems heavily rely on Deep Packet Inspection (DPI) techniques, such as Finite Automata (FA), to detect patterns carried by regular expression (regex). However, traditional Finite Automata cannot keep up with the ever-growing speed of the Internet links. Although there are a number of efficient FA compressing mechanisms for DPIs, there is no standardized or common way to evaluate and compare them. In this scenario, this paper proposes a methodology to evaluate and compare automaton models and the data-structures that materialize them. We also adapt state-of-the-art memory layouts to better fit in today's computer architectures. Finally, we apply our methodology to most important automaton models, memory layouts, and well-known signature sets. The results show us that some memory layouts are not efficient for regexes that represent small automata and other ones which fit only with uncompressed automata. Further, we also found out that theoretical studies about memory usage from memory encodings are not as accurate as they should be.
Year
DOI
Venue
2012
10.1109/GLOCOM.2012.6503370
Global Communications Conference
Keywords
Field
DocType
Internet,data compression,data structures,deterministic automata,finite automata,inspection,telecommunication traffic,DPI,FA compressing mechanisms,Internet links,automaton models,compressed DFA,computer architectures,data structure decoupling,deep packet inspection techniques,deterministic finite automata,memory encodings,memory layouts,network traffic analysis systems,regular expression,signature sets,traffic identification,DFA Models,Deep Packet Inspection,Performance Evaluation
Data structure,Deep packet inspection,Regular expression,Traffic analysis,Computer science,Automaton,Computer network,Real-time computing,Finite-state machine,Theoretical computer science,DFA minimization,Uncompressed video
Conference
ISSN
ISBN
Citations 
1930-529X E-ISBN : 978-1-4673-0919-6
978-1-4673-0919-6
1
PageRank 
References 
Authors
0.39
15
4
Name
Order
Citations
PageRank
Walcélio L. Melo11770119.97
Stenio Fernandes2713.26
Antonello, R.310.39
Sadok, D.410.39