An Efficient Real Time Intrusion Detection System for Big Data Environment

Faten Louati, Farah Barika Ktata, Ikram Amous

2023

Abstract

Nowadays, Security is among the most difficult issues in networks over the world. The problem becomes more challenging with the emergence of big data. Intrusion detection systems (IDSs) are among the most efficient solutions. However, traditional IDSs could not deal with big data challenges and are not able to detect attacks in real time. In this paper, a real time data preprocessing and attack detection are performed. Experiments on the well-known benchmark NSL KDD dataset show good results either in terms of accuracy rate or time of both training and testing and prove that our model outperforms other state-of-the-art solutions.

Paper Citation

in Harvard Style

Louati F., Barika Ktata F. and Amous I. (2023). An Efficient Real Time Intrusion Detection System for Big Data Environment. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 1004-1011. DOI: 10.5220/0011885900003393

in Bibtex Style

@conference{icaart23,
author={Faten Louati and Farah Barika Ktata and Ikram Amous},
title={An Efficient Real Time Intrusion Detection System for Big Data Environment},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={1004-1011},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011885900003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - An Efficient Real Time Intrusion Detection System for Big Data Environment
SN - 978-989-758-623-1
AU - Louati F.
AU - Barika Ktata F.
AU - Amous I.
PY - 2023
SP - 1004
EP - 1011
DO - 10.5220/0011885900003393