loading
Papers

Research.Publish.Connect.

Paper

Authors: Roberto Saia ; Salvatore Carta and Diego Reforgiato Recupero

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

ISBN: 978-989-758-330-8

Keyword(s): Intrusion Detection, Pattern Mining, Machine Learning, Ensemble Approach, Probabilistic Model.

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

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-worl d 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. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.172.195.49

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 - Volume 1: KDIR, ISBN 978-989-758-330-8, pages 141-148. DOI: 10.5220/0006893801410148

@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 - Volume 1: KDIR,},
year={2018},
pages={141-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006893801410148},
isbn={978-989-758-330-8},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - 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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.