Genetic Algorithm–Based Hyperparameter Optimization for 1D-CNN NIDS

Juraev G.U., Kilichev D., Mukhamadiyev A.N., Turimov D.M., Saydirasulov S.N.

Innovative Multidisciplinary Journal of Applied Technology · 2025-yil

Annotatsiya

This paper investigates Genetic Algorithm (GA)–driven hyperparameter optimization for one-dimensional Convolutional Neural Networks (1D-CNNs) in network intrusion detection systems (NIDS). Building on prior evidence that evolutionary search markedly improves deep models for NIDS, we optimize nine key hyperparameters (filters, kernel size, pooling size, dense depth/width, dropout, learning rate, batch size, and epochs) and train/evaluate on UNSW-NB15, CIC-IDS2017, and NSL-KDD using standard metrics: accuracy, loss, precision, recall, and F1-score. Our GA framework encodes hyperparameters as chromosomes and evolves candidates via selection, crossover, and mutation to maximize validation performance. Experiments show that GA-tuned 1D-CNNs consistently outperform non-optimized baselines across datasets; on UNSW-NB15, for example, GA attains ~99.31% accuracy, aligning with the best reported performance for GA-optimized 1D-CNNs. Results highlight that GA delivers robust gains with practical compute budgets while preserving strong precision-recall trade-offs, and that effectiveness can vary by dataset characteristics. Overall, GA-based hyperparameter optimization offers a simple, reproducible path to higher accuracy and reliability in deep learning–based NIDS, advancing the development of adaptable intrusion-detection solutions for evolving cyber threats.

Maqola ma’lumotlari
MualliflarJuraev G.U., Kilichev D., Mukhamadiyev A.N., Turimov D.M., Saydirasulov S.N.
JurnalInnovative Multidisciplinary Journal of Applied Technology
NashriyotТДТУ Қўқон филиали
ISSN2995-486X, 2995-486x
Nashr sanasi2025-09-30
Jild3
Son9
Betlar169-181
TilIngliz
Ilmiy sohaTexnika
DOI10.51699/80as1614
Indeksga qo‘shilgan2026-08-26

DOI: 10.51699/80as1614 · Maqolaning asl sahifasi

Kalit so‘zlar

intrusion-detection system (IDS), network intrusion detection system (NIDS), convolutional neural network (CNN), one-dimensional convolutional neural network (1D-CNN); hyperparameter optimization, genetic algorithm (GA)

Ilmiy soha

Texnika

Iqtibos keltirish

APAJuraev G.U., Kilichev D., Mukhamadiyev A.N., Turimov D.M., & Saydirasulov S.N. (2025). Genetic Algorithm–Based Hyperparameter Optimization for 1D-CNN NIDS. Innovative Multidisciplinary Journal of Applied Technology, 3(9), 169-181. https://doi.org/10.51699/80as1614
GOST (OAK)Juraev G.U. va b. Genetic Algorithm–Based Hyperparameter Optimization for 1D-CNN NIDS // Innovative Multidisciplinary Journal of Applied Technology. – 2025. – T. 3, № 9. – P. 169-181. – DOI: 10.51699/80as1614.
MLAJuraev G.U., Kilichev D., Mukhamadiyev A.N., Turimov D.M., and Saydirasulov S.N. “Genetic Algorithm–Based Hyperparameter Optimization for 1D-CNN NIDS.” Innovative Multidisciplinary Journal of Applied Technology, 3(9), 169-181, 2025. https://doi.org/10.51699/80as1614
ChicagoJuraev G.U., Kilichev D., Mukhamadiyev A.N., Turimov D.M., and Saydirasulov S.N. “Genetic Algorithm–Based Hyperparameter Optimization for 1D-CNN NIDS.” Innovative Multidisciplinary Journal of Applied Technology, 3(9), 169-181 (2025). https://doi.org/10.51699/80as1614
HarvardJuraev G.U., Kilichev D., Mukhamadiyev A.N., Turimov D.M., and Saydirasulov S.N (2025) ‘Genetic Algorithm–Based Hyperparameter Optimization for 1D-CNN NIDS’, Innovative Multidisciplinary Journal of Applied Technology, 3(9), 169-181. https://doi.org/10.51699/80as1614
BibTeX
@article{juraevgu2025,
  title = {Genetic Algorithm–Based Hyperparameter Optimization for 1D-CNN NIDS},
  author = {Juraev G.U. and Kilichev D. and Mukhamadiyev A.N. and Turimov D.M. and Saydirasulov S.N.},
  journal = {Innovative Multidisciplinary Journal of Applied Technology},
  year = {2025},
  volume = {3},
  number = {9},
  pages = {169-181},
  doi = {10.51699/80as1614},
  url = {https://doi.org/10.51699/80as1614}
}

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