Compressing Regular Expression Sets for Deep Packet Inspection
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Bartoli:2014:PPSN,
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author = "Alberto Bartoli and Simone Cumar and
Andrea {De Lorenzo} and Eric Medvet",
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title = "Compressing Regular Expression Sets for Deep Packet
Inspection",
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booktitle = "13th International Conference on Parallel Problem
Solving from Nature",
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year = "2014",
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editor = "Thomas Bartz-Beielstein and Juergen Branke and
Bogdan Filipic and Jim Smith",
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publisher = "Springer",
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isbn13 = "978-3-319-10761-5",
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pages = "394--403",
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series = "Lecture Notes in Computer Science",
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address = "Ljubljana, Slovenia",
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month = "13-17 " # sep,
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volume = "8672",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://machinelearning.inginf.units.it/publications/international-conference-publications/compressingregularexpressionsetsfordeeppacketinspection",
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DOI = "doi:10.1007/978-3-319-10762-2_39",
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abstract = "The ability to generate security-related alerts while
analysing network traffic in real time has become a key
mechanism in many networking devices. This
functionality relies on the application of large sets
of regular expressions describing attack signatures to
each individual packet. Implementing an engine of this
form capable of operating at line speed is considerably
difficult and the corresponding performance problems
have been attacked from several points of view. In this
work we propose a novel approach complementing earlier
proposals: we suggest transforming the starting set of
regular expressions to another set of expressions which
is much smaller yet classifies network traffic in the
same categories as the starting set. Key component of
the transformation is an evolutionary search based on
Genetic Programming: a large population of expressions
represented as abstract syntax trees evolves by means
of mutation and crossover, evolution being driven by
fitness indexes tailored to the desired classification
needs and which minimise the length of each expression.
We assessed our proposals on real datasets composed of
up to 474 expressions and the outcome has been very
good, resulting in compressions in the order of
74percent. Our results are highly encouraging and
demonstrate the power of evolutionary techniques in an
important application domain.",
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notes = "PPSN-XIII",
- }
Genetic Programming entries for
Alberto Bartoli
Simone Cumar
Andrea De Lorenzo
Eric Medvet
Citations