Optimized CNN-Based Intrusion Detection System Using Genetic Algorithm and Particle Swarm Optimization

Ayşe Keleş; İlya Kus; Cemal Koçak

doi:10.35377/saucis...1945980

Optimized CNN-Based Intrusion Detection System Using Genetic Algorithm and Particle Swarm Optimization

Abstract

Ensuring network security and preventing malicious attacks are crucial aspects of modern network management. In this context, various algorithms are employed to differentiate unauthorized access from authorized access, enabling monitoring of network traffic and detecting different types of attacks. However, traditional techniques have faced difficulty maintaining accuracy when predicting malicious activities. This study proposes an effective Intrusion Detection System mechanism designed to detect and classify attacks using the NSL-KDD and UNSW-NB15 datasets to address these challenges. The architecture of the proposed Convolutional Neural Network is optimized through hyperparameter tuning via Genetic Algorithm and Particle Swarm Optimization. As a result, the proposed approach achieves high-performance outcomes, attaining an accuracy of 99.80% with the NSL-KDD dataset and 99.02% with the UNSW-NB15 dataset. This innovative approach demonstrates significant improvement in identifying network intrusions compared to traditional methods. Consequently, it offers a reliable and efficient solution for enhancing network security, providing accurate detection of various attack types while minimizing false positives. The proposed model’s robust performance suggests that it can serve as an effective solution for real-time intrusion detection in modern network settings.

Keywords

Ethical Statement

It is declared that during the preparation process of this study, scientific and ethical principles were followed, and all the studies benefited from are stated in the bibliography.

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Details

Primary Language

English

Subjects

System and Network Security, Software and Application Security

Journal Section

Research Article

Authors

Ayşe Keleş ^*
0000-0001-8760-412X
Ireland

İlya Kus
0000-0001-5850-949X
Türkiye

Cemal Koçak
0000-0002-8902-0934
Türkiye

Early Pub Date

October 27, 2026

Publication Date

October 30, 2026

Submission Date

May 6, 2026

Acceptance Date

August 21, 2026

Published in Issue

Year 2026 Volume: 9 Number: 5

DOI

https://doi.org/10.35377/saucis...1945980

IZ

https://izlik.org/JA26AF72BP

Cite

RIS / Bibtex

APA

Keleş, A., Kus, İ., & Koçak, C. (2026). Optimized CNN-Based Intrusion Detection System Using Genetic Algorithm and Particle Swarm Optimization. Sakarya University Journal of Computer and Information Sciences, 9(5), 1512-1522. https://doi.org/10.35377/saucis...1945980

AMA

1.Keleş A, Kus İ, Koçak C. Optimized CNN-Based Intrusion Detection System Using Genetic Algorithm and Particle Swarm Optimization. SAUCIS. 2026;9(5):1512-1522. doi:10.35377/saucis.1945980

Chicago

Keleş, Ayşe, İlya Kus, and Cemal Koçak. 2026. “Optimized CNN-Based Intrusion Detection System Using Genetic Algorithm and Particle Swarm Optimization”. Sakarya University Journal of Computer and Information Sciences 9 (5): 1512-22. https://doi.org/10.35377/saucis. 1945980.

EndNote

Keleş A, Kus İ, Koçak C (October 1, 2026) Optimized CNN-Based Intrusion Detection System Using Genetic Algorithm and Particle Swarm Optimization. Sakarya University Journal of Computer and Information Sciences 9 5 1512–1522.

IEEE

[1]A. Keleş, İ. Kus, and C. Koçak, “Optimized CNN-Based Intrusion Detection System Using Genetic Algorithm and Particle Swarm Optimization”, SAUCIS, vol. 9, no. 5, pp. 1512–1522, Oct. 2026, doi: 10.35377/saucis...1945980.

ISNAD

Keleş, Ayşe - Kus, İlya - Koçak, Cemal. “Optimized CNN-Based Intrusion Detection System Using Genetic Algorithm and Particle Swarm Optimization”. Sakarya University Journal of Computer and Information Sciences 9/5 (October 1, 2026): 1512-1522. https://doi.org/10.35377/saucis. 1945980.

JAMA

1.Keleş A, Kus İ, Koçak C. Optimized CNN-Based Intrusion Detection System Using Genetic Algorithm and Particle Swarm Optimization. SAUCIS. 2026;9:1512–1522.

MLA

Keleş, Ayşe, et al. “Optimized CNN-Based Intrusion Detection System Using Genetic Algorithm and Particle Swarm Optimization”. Sakarya University Journal of Computer and Information Sciences, vol. 9, no. 5, Oct. 2026, pp. 1512-2, doi:10.35377/saucis. 1945980.

Vancouver

1.Ayşe Keleş, İlya Kus, Cemal Koçak. Optimized CNN-Based Intrusion Detection System Using Genetic Algorithm and Particle Swarm Optimization. SAUCIS. 2026 Oct. 1;9(5):1512-2. doi:10.35377/saucis. 1945980