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Authors: Nesrine Kaaniche 1 ; Aymen Boudguiga 2 and Gustavo Gonzalez-Granadillo 3

Affiliations: 1 SAMOVAR, Telecom SudParis, Institut Polytechnique de Paris, France ; 2 Université Paris-Saclay, CEA-List, Palaiseau, France ; 3 Atos Research & Innovation, Cybersecurity Laboratory, Spain

Keyword(s): IDS, K-Means, Bayesian Model, Hybrid Approach, Supervised and Unsupervised Learning.

Abstract: This paper proposes a new hybrid ML model that relies on K-Means clustering and the Variational Bayesian Gaussian Mixture models to efficiently detect and classify unknown network attacks. The proposed model first classifies the input data into various clusters using K-Means. Then, it identifies anomalies in those clusters using the Variational Bayesian Gaussian Mixture model. The model has been tested against the CICIDS 2017 dataset that contains new relevant attacks and realistic normal traffic, with a reasonable size. To balance the data, undersampling techniques were used. Furthermore, the features were reduced from 78 to 28 using feature selection and feature extraction methods. The proposed model shows promising results when identifying whether a data point is an attack or not with an F1 score of up to 91%.

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Paper citation in several formats:
Kaaniche, N., Boudguiga, A. and Gonzalez-Granadillo, G. (2022). Efficient Hybrid Model for Intrusion Detection Systems. In Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-590-6; ISSN 2184-7711, SciTePress, pages 694-700. DOI: 10.5220/0011328300003283

@conference{secrypt22,
author={Nesrine Kaaniche and Aymen Boudguiga and Gustavo Gonzalez{-}Granadillo},
title={Efficient Hybrid Model for Intrusion Detection Systems},
booktitle={Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT},
year={2022},
pages={694-700},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011328300003283},
isbn={978-989-758-590-6},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT
TI - Efficient Hybrid Model for Intrusion Detection Systems
SN - 978-989-758-590-6
IS - 2184-7711
AU - Kaaniche, N.
AU - Boudguiga, A.
AU - Gonzalez-Granadillo, G.
PY - 2022
SP - 694
EP - 700
DO - 10.5220/0011328300003283
PB - SciTePress

- Science and Technology Publications, Lda.
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