@article{gmacia_ugr16_2017,
author = "Maci{\'a}-Fern{\'a}ndez, Gabriel  and Camacho, Jos{\'e} and Mag{\'a}n-Carri{\'o}n, Roberto  and Garc{\'i}a-Teodoro, Pedro  and Ther{\'o}n S{\'a}nchez, Roberto",
abstract = "The evaluation of algorithms and techniques to implement intrusion detection systems heavily rely on the existence of well designed datasets. In the last years, a lot of efforts have been done towards building these datasets. Yet, there is still room to improve. In this paper, a comprehensive review of existing datasets is first done, making emphasis on their main shortcomings. Then, we present a new dataset that is built with real traffic and up-to-date attacks. The main advantage of this dataset over previous ones is its usefulness for evaluating IDSs that consider long-term evolution and traffic periodicity. Models that consider differences in daytime/night or weekdays/weekends can also be trained and evaluated with it. We discuss all the requirements for a modern IDS evaluation dataset and analyze how the one presented here meets the different needs.",
doi = "10.1016/j.cose.2017.11.004",
issn = "0167-4048",
journal = "Computer {\&} Security",
keywords = "dataset;IDS;network attacks;security",
month = "November",
pages = "411-424",
title = "{U}gr'16: a new dataset for the evaluation of cyclostationarity-based network {IDS}s",
url = "http://www.sciencedirect.com/science/article/pii/S0167404817302353",
volume = "73",
year = "2018",
}