Generating mimicry attacks using genetic programming: A benchmarking study

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Gunes-Kayacik:2009:ieeeCICS,

• author = "H. {Gunes Kayacik} and A. Nur Zincir-Heywood and Malcolm I. Heywood and Stefan Burschka",
• title = "Generating mimicry attacks using genetic programming: A benchmarking study",
• booktitle = "IEEE Symposium on Computational Intelligence in Cyber Security, CICS '09",
• year = "2009",
• month = "30 " # mar # "-" # apr # " 2",
• pages = "136--143",
• 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",
• DOI = "doi:10.1109/CICYBS.2009.4925101",
• 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.",
• notes = "Also known as \cite{4925101}",

}

Genetic Programming entries for Hilmi Gunes Kayacik Nur Zincir-Heywood Malcolm Heywood Stefan Burschka

Citations