DocumentCode
2015024
Title
FlowSifter: A counting automata approach to layer 7 field extraction for deep flow inspection
Author
Meiners, Chad ; Norige, Eric ; Liu, Alex X. ; Torng, Eric
Author_Institution
Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA
fYear
2012
fDate
25-30 March 2012
Firstpage
1746
Lastpage
1754
Abstract
In this paper, we introduce FlowSifter, a systematic framework for online application protocol field extraction. FlowSifter introduces a new grammar model Counting Regular Grammars (CRG) and a corresponding automata model Counting Automata (CA). The CRG and CA models add counters with update functions and transition guards to regular grammars and finite state automata. These additions give CRGs and CAs the ability to parse and extract fields from context sensitive application protocols. These additions also facilitate fast and stackless approximate parsing of recursive structures. These new grammar models enable FlowSifter to generate optimized Layer 7 field extractors from simple extraction specifications. In our experiments, we compare FlowSifter against both BinPAC and UltraPAC, which are the freely available state of the art field extractors. Our experiments show that when compared to UltraPAC parsers, FlowSifter extractors run 84% faster and use 12% of the memory.
Keywords
context-free grammars; finite state machines; protocols; FlowSifter; context sensitive application protocol; counting automata approach; counting regular grammar; deep flow inspection; extraction specification; finite state automata; grammar model; layer 7 field extraction; online application protocol field extraction; recursive structure; stackless approximate parsing; transition guard; update function; Approximation methods; Automata; Data mining; Grammar; Production; Protocols; Radiation detectors;
fLanguage
English
Publisher
ieee
Conference_Titel
INFOCOM, 2012 Proceedings IEEE
Conference_Location
Orlando, FL
ISSN
0743-166X
Print_ISBN
978-1-4673-0773-4
Type
conf
DOI
10.1109/INFCOM.2012.6195547
Filename
6195547
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