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Authors: Roberto Saia ; Salvatore Carta ; Diego Recupero ; Gianni Fenu and Maria Stanciu

Affiliation: Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari and Italy

ISBN: 978-989-758-382-7

Keyword(s): Machine Learning, Anomaly Detection, Pattern Recognition.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Clustering and Classification Methods ; Computational Intelligence ; Data Analytics ; Data Engineering ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Structured Data Analysis and Statistical Methods ; Symbolic Systems

Abstract: The unbreakable bond that exists today between devices and network connections makes the security of the latter a crucial element for our society. For this reason, in recent decades we have witnessed an exponential growth in research efforts aimed at identifying increasingly efficient techniques able to tackle this type of problem, such as the Intrusion Detection System (IDS). If on the one hand an IDS plays a key role, since it is designed to classify the network events as normal or intrusion, on the other hand it has to face several well-known problems that reduce its effectiveness. The most important of them is the high number of false positives related to its inability to detect event patterns not occurred in the past (i.e. zero-day attacks). This paper introduces a novel Discretized Extended Feature Space (DEFS) model that presents a twofold advantage: first, through a discretization process it reduces the event patterns by grouping those similar in terms of feature values, reduc ing the issues related to the classification of unknown events; second, it balances such a discretization by extending the event patterns with a series of meta-information able to well characterize them. The approach has been evaluated by using a real-world dataset (NSL-KDD) and by adopting both the in-sample/out-of-sample and time series cross-validation strategies in order to avoid that the evaluation is biased by over-fitting. The experimental results show how the proposed DEFS model is able to improve the classification performance in the most challenging scenarios (unbalanced samples), with regard to the canonical state-of-the-art solutions. (More)

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Paper citation in several formats:
Saia, R.; Carta, S.; Recupero, D.; Fenu, G. and Stanciu, M. (2019). A Discretized Extended Feature Space (DEFS) Model to Improve the Anomaly Detection Performance in Network Intrusion Detection Systems.In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-382-7, pages 322-329. DOI: 10.5220/0008113603220329

@conference{kdir19,
author={Roberto Saia. and Salvatore Carta. and Diego Reforgiato Recupero. and Gianni Fenu. and Maria Madalina Stanciu.},
title={A Discretized Extended Feature Space (DEFS) Model to Improve the Anomaly Detection Performance in Network Intrusion Detection Systems},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2019},
pages={322-329},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008113603220329},
isbn={978-989-758-382-7},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - A Discretized Extended Feature Space (DEFS) Model to Improve the Anomaly Detection Performance in Network Intrusion Detection Systems
SN - 978-989-758-382-7
AU - Saia, R.
AU - Carta, S.
AU - Recupero, D.
AU - Fenu, G.
AU - Stanciu, M.
PY - 2019
SP - 322
EP - 329
DO - 10.5220/0008113603220329

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