A Feature Space Transformation to Intrusion Detection Systems

Roberto Saia, Salvatore Carta, Diego Reforgiato Recupero, Gianni Fenu

2020

Abstract

The anomaly-based Intrusion Detection Systems (IDSs) represent one of the most efficient methods in countering the intrusion attempts against the ever growing number of network-based services. Despite the central role they play, their effectiveness is jeopardized by a series of problems that reduce the IDS effectiveness in a real-world context, mainly due to the difficulty of correctly classifying attacks with characteristics very similar to a normal network activity or, again, due to the difficulty of contrasting novel forms of attacks (zero-days). Such problems have been faced in this paper by adopting a Twofold Feature Space Transformation (TFST) approach aimed to gain a better characterization of the network events and a reduction of their potential patterns. The idea behind such an approach is based on: (i) the addition of meta-information, improving the event characterization; (ii) the discretization of the new feature space in order to join together patterns that lead back to the same events, reducing the number of false alarms. The validation process performed by using a real-world dataset indicates that the proposed approach is able to outperform the canonical state-of-the-art solutions, improving their intrusion detection capability.

Paper Citation

in Harvard Style

Saia R., Carta S., Recupero D. and Fenu G. (2020). A Feature Space Transformation to Intrusion Detection Systems. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR; ISBN 978-989-758-474-9, SciTePress, pages 137-144. DOI: 10.5220/0009982901370144

in Bibtex Style

@conference{kdir20,
author={Roberto Saia and Salvatore Carta and Diego Reforgiato Recupero and Gianni Fenu},
title={A Feature Space Transformation to Intrusion Detection Systems},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR},
year={2020},
pages={137-144},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009982901370144},
isbn={978-989-758-474-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR
TI - A Feature Space Transformation to Intrusion Detection Systems
SN - 978-989-758-474-9
AU - Saia R.
AU - Carta S.
AU - Recupero D.
AU - Fenu G.
PY - 2020
SP - 137
EP - 144
DO - 10.5220/0009982901370144
PB - SciTePress