A Technical Survey on Intelligent Optimization Grouping Algorithms for Finite State Automata in Deep Packet Inspection

Samuel, P; Subbaiyan, S; Balusamy, B; Doraikannan, S; Gandomi, AH

A Technical Survey on Intelligent Optimization Grouping Algorithms for Finite State Automata in Deep Packet Inspection

Samuel, P Subbaiyan, S Balusamy, B Doraikannan, S Gandomi, AH

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Archives of Computational Methods in Engineering, 2020
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Samuel, P
dc.contributor.author	Subbaiyan, S
dc.contributor.author	Balusamy, B
dc.contributor.author	Doraikannan, S
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2020-10-23T02:31:44Z
dc.date.available	2020-10-23T02:31:44Z
dc.date.issued	2020-01-01
dc.identifier.citation	Archives of Computational Methods in Engineering, 2020
dc.identifier.issn	1134-3060
dc.identifier.issn	1886-1784
dc.identifier.uri	http://hdl.handle.net/10453/143490
dc.description.abstract	© 2020, CIMNE, Barcelona, Spain. Construction and deployment of finite state automata from the regular expressions might results in huge overhead and results in the state explosion problem which is in need of large memory space, high bandwidth and additional computational time. To overcome this problem, a new framework is proposed, and several intelligent optimization algorithms are reviewed and compared in this framework. The proposed approach is called intelligent optimization grouping algorithms (IOGA), which intends to group regular expression intelligently. IOGAs are used to allocate the regular expression sets into various groups and to build independent deterministic finite automata (DFA) for each group. Grouping the regular expression efficiently solves the state explosion problem by achieving large-scale best tradeoff among memory utilization and computational time. This study reviews and compares the various alternatives of IOGA including genetic algorithm, ant colony optimization, particle swarm optimization, bacterial foraging optimization, artificial bee colony algorithm, biogeography based optimization, cuckoo search, firefly algorithm, bat algorithm and flower pollination algorithm for solving the problem of DFA state explosion and also for improving the overall efficiency of deep packet inspection (DPI). The discussions state that by effectively using these grouping algorithms along with DFA based DPI, the number of states can be reduced, providing a balance between the memory consumption, time complexity, throughput, inspection speed, convergence speed and grouping time.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Archives of Computational Methods in Engineering
dc.relation.isbasedon	10.1007/s11831-020-09419-z
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Applied Mathematics
dc.title	A Technical Survey on Intelligent Optimization Grouping Algorithms for Finite State Automata in Deep Packet Inspection
dc.type	Journal Article
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-23T02:31:08Z
pubs.publication-status	Published

Abstract:

© 2020, CIMNE, Barcelona, Spain. Construction and deployment of finite state automata from the regular expressions might results in huge overhead and results in the state explosion problem which is in need of large memory space, high bandwidth and additional computational time. To overcome this problem, a new framework is proposed, and several intelligent optimization algorithms are reviewed and compared in this framework. The proposed approach is called intelligent optimization grouping algorithms (IOGA), which intends to group regular expression intelligently. IOGAs are used to allocate the regular expression sets into various groups and to build independent deterministic finite automata (DFA) for each group. Grouping the regular expression efficiently solves the state explosion problem by achieving large-scale best tradeoff among memory utilization and computational time. This study reviews and compares the various alternatives of IOGA including genetic algorithm, ant colony optimization, particle swarm optimization, bacterial foraging optimization, artificial bee colony algorithm, biogeography based optimization, cuckoo search, firefly algorithm, bat algorithm and flower pollination algorithm for solving the problem of DFA state explosion and also for improving the overall efficiency of deep packet inspection (DPI). The discussions state that by effectively using these grouping algorithms along with DFA based DPI, the number of states can be reduced, providing a balance between the memory consumption, time complexity, throughput, inspection speed, convergence speed and grouping time.

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http://hdl.handle.net/10453/143490