Generating mimicry attacks using genetic programming: A benchmarking study
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Gunes-Kayacik:2009:ieeeCICS,
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author = "H. {Gunes Kayacik} and A. Nur Zincir-Heywood and
Malcolm I. Heywood and Stefan Burschka",
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title = "Generating mimicry attacks using genetic programming:
A benchmarking study",
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booktitle = "IEEE Symposium on Computational Intelligence in Cyber
Security, CICS '09",
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year = "2009",
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month = "30 " # mar # "-" # apr # " 2",
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pages = "136--143",
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keywords = "genetic algorithms, genetic programming, benchmark
testing, black-box approach, commodity anomaly
detection system, evolutionary mimicry attack
generation, intrusion detection, multiobjective genetic
programming, open-source anomaly detection system,
penetration testing, target anomaly detection,
vulnerability testing approach, vulnerable UNIX
application, benchmark testing, program testing,
security of data",
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DOI = "doi:10.1109/CICYBS.2009.4925101",
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abstract = "Mimicry attacks have been the focus of detector
research where the objective of the attacker is to
generate multiple attacks satisfying the same generic
exploit goals for a given vulnerability. In this work,
multi-objective Genetic programming is used to
establish a black-box approach to mimicry attack
generation. No knowledge is made of internal data
structures of the target anomaly detector, only the
anomaly rate reported by the detector. Such a 'black
box' methodology enables a vulnerability testing
approach where both open-source and commodity anomaly
detection systems can be tested. The approach
successfully identifies exploits when benchmarked over
four detectors and four applications.",
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notes = "Also known as \cite{4925101}",
- }
Genetic Programming entries for
Hilmi Gunes Kayacik
Nur Zincir-Heywood
Malcolm Heywood
Stefan Burschka
Citations