Learning From Evolving Network Data for Dependable Botnet Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Le:2019:CNSM,

• author = "Duc C. Le and Nur Zincir-Heywood",
• title = "Learning From Evolving Network Data for Dependable Botnet Detection",
• booktitle = "2019 15th International Conference on Network and Service Management (CNSM)",
• year = "2019",
• month = oct,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.23919/CNSM46954.2019.9012710",
• ISSN = "2165-963X",
• 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.",
• notes = "Also known as \cite{9012710}",

}

Genetic Programming entries for Duc C Le Nur Zincir-Heywood

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