Learning From Evolving Network Data for Dependable Botnet Detection
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Le:2019:CNSM,
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author = "Duc C. Le and Nur Zincir-Heywood",
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title = "Learning From Evolving Network Data for Dependable
Botnet Detection",
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booktitle = "2019 15th International Conference on Network and
Service Management (CNSM)",
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year = "2019",
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month = oct,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.23919/CNSM46954.2019.9012710",
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ISSN = "2165-963X",
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abstract = "This work presents an emerging problem in real-world
applications of machine learning (ML) in cybersecurity,
particularly in botnet detection, where the dynamics
and the evolution in the deployment environments may
render the ML solutions inadequate. We propose an
approach to tackle this challenge using Genetic
Programming (GP) - an evolutionary computation based
approach. Preliminary results show that GP is able to
evolve pre-trained classifiers to work under evolved
(expanded) feature space conditions. This indicates the
potential use of such an approach for botnet detection
under non-stationary environments, where much less data
and training time are required to obtain a reliable
classifier as new network conditions arise.",
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notes = "Also known as \cite{9012710}",
- }
Genetic Programming entries for
Duc C Le
Nur Zincir-Heywood
Citations