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Communication Dans Un Congrès Année : 2022

Efficient hybrid model for intrusion detection systems

Résumé

This paper proposes a new hybrid ML model that relies on both K-Means clustering and the Variational Bayesian Gaussian Mixture model to efficiently detect 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, to be then classified as unknown. 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\%, such that the Variational Bayesian Gaussian Mixture model detected up to 86\% of unknown attacks. The conducted experiments shows acceptable performances, where the predictive pipeline took around 2.42 seconds to be processed.
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Dates et versions

hal-04137867 , version 1 (22-06-2023)

Identifiants

Citer

Nesrine Kaaniche, Aymen Boudguiga, Gustavo Gonzalez-Granadillo. Efficient hybrid model for intrusion detection systems. 19th International Conference on Security and Cryptography (SECRYPT), Jul 2022, Lisbon, Portugal. pp.694-700, ⟨10.5220/0011328300003283⟩. ⟨hal-04137867⟩
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