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Authors: German Lopez-Civera and Enrique de la Hoz

Affiliation: University of Alcala, Spain

ISBN: 978-989-758-196-0

Keyword(s): Intrusion Detection, Dataset Evaluation, Machine Learning, Decision Tree.

Abstract: Anomaly Detection techniques allow to create robust security measures that provides early detection and are able to identify novel attacks that could not be prevented otherwise. Datasets represent a critical component in the process of designing and evaluating any kind of anomaly detection method. For this reason, in this paper we present the evaluation of two datasets showing the dependencies that arise between the techniques employed and the dataset itself. We also describe the characteristics that have to be taken into account while selecting a dataset to evaluate a detection algorithm in a critical infrastructure context.

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Paper citation in several formats:
Lopez-Civera, G. and de la Hoz, E. (2016). Dataset Analysis for Anomaly Detection on Critical Infrastructures.In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications - Volume 3: DCCI, (ICETE 2016) ISBN 978-989-758-196-0, pages 151-158. DOI: 10.5220/0006017701510158

@conference{dcci16,
author={German Lopez{-}Civera. and Enrique de la Hoz.},
title={Dataset Analysis for Anomaly Detection on Critical Infrastructures},
booktitle={Proceedings of the 13th International Joint Conference on e-Business and Telecommunications - Volume 3: DCCI, (ICETE 2016)},
year={2016},
pages={151-158},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006017701510158},
isbn={978-989-758-196-0},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications - Volume 3: DCCI, (ICETE 2016)
TI - Dataset Analysis for Anomaly Detection on Critical Infrastructures
SN - 978-989-758-196-0
AU - Lopez-Civera, G.
AU - de la Hoz, E.
PY - 2016
SP - 151
EP - 158
DO - 10.5220/0006017701510158

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