An Incremental Ensemble Evolved by using Genetic Programming to Efficiently Detect Drifts in Cyber Security Datasets

Abstract

Unbalanced classes, the ability to detect changes in real-time, the speed of the streams and other peculiar characteristics make most of the data mining algorithms not apt to operate with datasets in the cyber security domain. To overcome these issues, we propose an ensemble-based algorithm, using a distributed Genetic Programming framework to generate the… (More)
DOI: 10.1145/2908961.2931682

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Cite this paper

@inproceedings{Folino2016AnIE, title={An Incremental Ensemble Evolved by using Genetic Programming to Efficiently Detect Drifts in Cyber Security Datasets}, author={Gianluigi Folino and Francesco Sergio Pisani and Pietro Sabatino}, booktitle={GECCO}, year={2016} }