Skip to main navigation menu Skip to main content Skip to site footer
×
Español (España) | English
Editorial
Home
Indexing
Original

Fine-Tuning CNN-BiGRU for Intrusion Detection with SMOTE Optimization Using Optuna

By
Asmaa Benchama ,
Asmaa Benchama

IMISR Laboratory, Faculty of Science AM, Ibn Zohr University, Agadir, Morocco

Search this author on:

PubMed | Google Scholar
Khalid Zebbara ,
Khalid Zebbara

IMISR Laboratory, Faculty of Science AM, Ibn Zohr University, Agadir, Morocco

Search this author on:

PubMed | Google Scholar

Abstract

Network security faces a significant challenge in developing effective models for intrusion detection within network systems. Network Intrusion Detection Systems (NIDS) are vital for protecting network traffic and preempting potential attacks by identifying signatures and rule violations.
This research aims to enhance intrusion detection using Deep learning techniques, particularly by employing the NSLKDD dataset to train and evaluate a hybrid CNN-BiGRU algorithm. Additionally, we utilize the Synthetic Minority Over-sampling Technique (SMOTE) to address imbalanced data and Optuna for fine-tuning the algorithm's parameters specific to NIDS requirements.
The hybrid CNN-BiGRU algorithm is trained and evaluated on the NSLKDD dataset, incorporating SMOTE to tackle imbalanced data issues. Optuna is utilized to optimize the algorithm's parameters for improved performance in intrusion detection.
Experimental results demonstrate that our approach surpasses classical intrusion detection models. Achieving an accuracy rate of 98.83% on NSLKDD, the proposed model excels in identifying minority attacks while maintaining a low false positive rate.
The findings affirm the efficacy of our proposed approach in network intrusion detection, showcasing its ability to effectively discern patterns in network traffic and outperform traditional models.

How to Cite

1.
Benchama A, Zebbara K. Fine-Tuning CNN-BiGRU for Intrusion Detection with SMOTE Optimization Using Optuna. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Jun. 29 [cited 2024 Jul. 19];3:968. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/968

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

Article metrics

Google scholar: See link

Metrics

Metrics Loading ...

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.