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Authors: Faten Louati 1 ; 2 ; Farah Barika Ktata 3 ; 2 and Ikram Amous Ben Amor 2 ; 4

Affiliations: 1 Faculty of Economics and Management of Sfax, Tunisia ; 2 Multimedia, InfoRmation Systems and Advanced Computing Laboratory (MIRACL), Tunisia ; 3 Higher Institute of Applied Sciences and Technology of Sousse, Tunisia ; 4 National School of Electronics and Telecommunications of Sfax, Tunisia

Keyword(s): Intrusion Detection, Big Data, Reinforcement Learning, Multi Agent System.

Abstract: Networking security continue to be a serious challenge for all domains because of the increasing number of attacks launched every day due to the advent of connected devices and the emergence of the Internet. Hence, Intrusion detection system comes into focus, especially with the inception of big data challenges. In this paper, we propose a distributed and parallel intrusion detection system suitable for big data environments using machine learning-based multi agent system and big data analysis.

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Paper citation in several formats:
Louati, F.; Ktata, F. and Ben Amor, I. (2022). A Distributed Intelligent Intrusion Detection System based on Parallel Machine Learning and Big Data Analysis. In Proceedings of the 11th International Conference on Sensor Networks - SENSORNETS; ISBN 978-989-758-551-7; ISSN 2184-4380, SciTePress, pages 152-157. DOI: 10.5220/0010886300003118

@conference{sensornets22,
author={Faten Louati. and Farah Barika Ktata. and Ikram Amous {Ben Amor}.},
title={A Distributed Intelligent Intrusion Detection System based on Parallel Machine Learning and Big Data Analysis},
booktitle={Proceedings of the 11th International Conference on Sensor Networks - SENSORNETS},
year={2022},
pages={152-157},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010886300003118},
isbn={978-989-758-551-7},
issn={2184-4380},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Sensor Networks - SENSORNETS
TI - A Distributed Intelligent Intrusion Detection System based on Parallel Machine Learning and Big Data Analysis
SN - 978-989-758-551-7
IS - 2184-4380
AU - Louati, F.
AU - Ktata, F.
AU - Ben Amor, I.
PY - 2022
SP - 152
EP - 157
DO - 10.5220/0010886300003118
PB - SciTePress