Abstract
Classification is a relevant task in the cyber security domain, but it must be able to cope with unbalanced and/or incomplete datasets and must also react in real-time to changes in the data. Ensemble of classifiers are a useful tool for classification in hard domains as they combine different classifiers that together provide complementary information. However, most of the ensemble-based algorithms require an extensive training phase and need to be re-trained in case of changes in the data.
This work proposes a Genetic Programming-based framework to generate a function for combining an ensemble, having some interesting properties: the models composing the ensemble are trained only on a portion of the training set, and then, they can be combined and used without any extra phase of training; furthermore, in case of changes in the data, the function can be recomputed in an incrementally way, with a moderate computational effort.
Experiments conducted on unbalanced datasets and on a well-known cyber-security dataset assess the goodness of the approach.
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Acknowledgment
This work has been partially supported by MIUR-PON under project PON03PE_00032_2 within the framework of the Technological District on Cyber Security.
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Folino, G., Pisani, F.S. (2015). Combining Ensemble of Classifiers by Using Genetic Programming for Cyber Security Applications. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_5
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DOI: https://doi.org/10.1007/978-3-319-16549-3_5
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