A Probabilistic-driven Ensemble Approach to Perform Event Classification in Intrusion Detection System

Roberto Saia, Salvatore Carta, Diego Reforgiato Recupero

2018

Abstract

Nowadays, it is clear how the network services represent a widespread element, which is absolutely essential for each category of users, professional and non-professional. Such a scenario needs a constant research activity aimed to ensure the security of the involved services, so as to prevent any fraudulently exploitation of the related network resources. This is not a simple task, because day by day new threats arise, forcing the research community to face them by developing new specific countermeasures. The Intrusion Detection System (IDS) covers a central role in this scenario, as its main task is to detect the intrusion attempts through an evaluation model designed to classify each new network event as normal or intrusion. This paper introduces a Probabilistic-Driven Ensemble (PDE) approach that operates by using several classification algorithms, whose effectiveness has been improved on the basis of a probabilistic criterion. A series of experiments, performed by using real-world data, show how such an approach outperforms the state-of-the-art competitors, proving its better capability to detect intrusion events with regard to the canonical solutions.

Paper Citation

in Harvard Style

Saia R., Carta S. and Recupero D. (2018). A Probabilistic-driven Ensemble Approach to Perform Event Classification in Intrusion Detection System. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 1: KDIR; ISBN 978-989-758-330-8, SciTePress, pages 141-148. DOI: 10.5220/0006893801410148

in Bibtex Style

@conference{kdir18,
author={Roberto Saia and Salvatore Carta and Diego Reforgiato Recupero},
title={A Probabilistic-driven Ensemble Approach to Perform Event Classification in Intrusion Detection System},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 1: KDIR},
year={2018},
pages={141-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006893801410148},
isbn={978-989-758-330-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 1: KDIR
TI - A Probabilistic-driven Ensemble Approach to Perform Event Classification in Intrusion Detection System
SN - 978-989-758-330-8
AU - Saia R.
AU - Carta S.
AU - Recupero D.
PY - 2018
SP - 141
EP - 148
DO - 10.5220/0006893801410148
PB - SciTePress