A Novel Histogram-based Network Anomaly Detection

Christian Callegari; Michele Pagano; Stefano Giordano; Fabrizio Berizzi

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Paper

A Novel Histogram-based Network Anomaly Detection

In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications - Volume 4: DCCI, 103-110, 2016 , Lisbon, Portugal

Authors: Christian Callegari ¹ ; Michele Pagano ² ; Stefano Giordano ² and Fabrizio Berizzi ¹

Affiliations: ¹ RaSS National Laboratory – CNIT and University of Pisa, Italy ; ² University of Pisa, Italy

Keyword(s): Anomaly Detection, Histogram, Euclidean Distance, Kullback–Leibler Divergence, Jansen–Shannon Divergence.

Abstract: The ability of capturing unknown attacks is an attractive feature of anomaly-based intrusion detection and it is not surprising that research on such a topic represents one of the most promising directions in the field of network security. In this work we consider two different traffic descriptors and evaluate their ability in capturing different kinds of anomalies, taking into account three different measures of similarity in order to discriminate between the normal network behaviour and the presence of anomalies. An extensive performance analysis, carried out over the publicly available MAWILab dataset, has highlighted that a proper choice of the relevant traffic descriptor and the similarity measure can be particularly efficient in the case of unknown attacks, i.e. those attacks that cannot be detected by standard misuse-based systems.

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Paper citation in several formats:

Callegari, C.; Pagano, M.; Giordano, S. and Berizzi, F. (2016). A Novel Histogram-based Network Anomaly Detection. In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - DCCI; ISBN 978-989-758-196-0; ISSN 2184-3236, SciTePress, pages 103-110. DOI: 10.5220/0006013401030110

@conference{dcci16,
author={Christian Callegari. and Michele Pagano. and Stefano Giordano. and Fabrizio Berizzi.},
title={A Novel Histogram-based Network Anomaly Detection},
booktitle={Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - DCCI},
year={2016},
pages={103-110},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006013401030110},
isbn={978-989-758-196-0},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - DCCI
TI - A Novel Histogram-based Network Anomaly Detection
SN - 978-989-758-196-0
IS - 2184-3236
AU - Callegari, C.
AU - Pagano, M.
AU - Giordano, S.
AU - Berizzi, F.
PY - 2016
SP - 103
EP - 110
DO - 10.5220/0006013401030110
PB - SciTePress