@inbook{0af5a3df137a47cdb98991b63914abfc,
title = "Learning Flow Characteristics Distributions with ELM for Distributed Denial of Service Detection and Mitigation",
abstract = "We present a methodology for modeling the distributions of network flow statistics for the specific purpose of network anomaly detection, in the form of Distributed Denial of Service attacks. The proposed methodology offers to model (using Extreme Learning Machines, ELM), at the IP subnetwork level (or all the way down to the single IP level, if computations allow), the usual distributions of certain network flow characteristics (or statistics), and then to use a One-Class classifier in the detection of abnormal joint flow statistics. The methodology makes use of the original ELM for its good performance to computational time ratio, but also because of the needs in this methodology to have simple update rules for making the model evolve in time, as new traffic and hosts come in.",
author = "Aapo Kalliola and Yoan Miche and Ian Oliver and Silke Holtmanns and Buse Atli and Amaury Lendasse and Kaj-Mikael Bjork and Anton Akusok and Tuomas Aura",
year = "2018",
doi = "10.1007/978-3-319-57421-9_11",
language = "English",
isbn = "978-3-319-57421-9",
series = "Proceedings in Adaptation, Learning and Optimization",
publisher = "SPRINGER",
pages = "129--143",
editor = "Jiuwen Cao and Erik Cambria and Amaury Lendasse and Yoan Miche and Vong, {Chi Man}",
booktitle = "Proceedings of ELM-2016",
address = "Germany",
note = "International Conference on Extreme Learning Machines, ELM ; Conference date: 13-12-2016 Through 15-12-2016",
}