- author = "Gianluigi Folino and Francesco Sergio Pisani and Pietro Sabatino",
- title = "An Incremental Ensemble Evolved by using Genetic Programming to Efficiently Detect Drifts in Cyber Security Datasets",
- booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation",
- year = "2016",
- isbn13 = "978-1-4503-4323-7",
- pages = "1103--1110",
- keywords = "genetic algorithms, genetic programming",
- month = "20-24 " # jul,
- organisation = "SIGEVO",
- address = "Denver, Colorado, USA",
-
DOI = "
doi:10.1145/2908961.2931682",
- publisher = "ACM",
-
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 function to
combine the classifiers and efficient strategies to
react to changes in data. After that the base
classifiers are trained, the combining function of the
ensemble, based on non-trainable functions, can be
generated without any extra phase of training, while
the drift detection function adopted, together with a
strategy for replacing classifiers, permits to respond
in an efficient way to changes.
Preliminary experiments conducted on an artificial dataset and on a real intrusion detection dataset show the effectiveness of the approach.",
- publisher_address = "New York, NY, USA",
