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.
| Mualliflar | Juraev G.U., Kilichev D., Mukhamadiyev A.N., Turimov D.M., Saydirasulov S.N. |
|---|---|
| Jurnal | Innovative Multidisciplinary Journal of Applied Technology |
| Nashr sanasi | 2025-09-30 |
| Jild | 3 |
| Son | 9 |
| Betlar | 169-181 |
| Til | Ingliz |
| DOI | 10.51699/80as1614 |
DOI: 10.51699/80as1614 · Maqolaning asl sahifasi
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)
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