TY - GEN
T1 - Efficient Hybrid Model for Intrusion Detection Systems
AU - Kaaniche, Nesrine
AU - Boudguiga, Aymen
AU - Gonzalez-Granadillo, Gustavo
N1 - Publisher Copyright:
© 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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%.
AB - 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%.
KW - Bayesian Model
KW - Hybrid Approach
KW - IDS
KW - K-Means
KW - Supervised and Unsupervised Learning
UR - https://www.scopus.com/pages/publications/85178505738
U2 - 10.5220/0011328300003283
DO - 10.5220/0011328300003283
M3 - Conference contribution
AN - SCOPUS:85178505738
SN - 9789897585906
T3 - Proceedings of the International Conference on Security and Cryptography
SP - 694
EP - 700
BT - SECRYPT 2022 - Proceedings of the 19th International Conference on Security and Cryptography
A2 - De Capitani di Vimercati, Sabrina
A2 - Samarati, Pierangela
PB - Science and Technology Publications, Lda
T2 - 19th International Conference on Security and Cryptography, SECRYPT 2022
Y2 - 11 July 2022 through 13 July 2022
ER -