Enhancing CNN-based network intrusion detection through hyperparameter optimization
Abstracts: This research investigates the optimization of hyperparameters in Convolutional Neural Networks (CNNs) to enhance the performance of Network Intrusion Detection Systems (NIDS). Four distinct optimization techniques, including Grid Search (GS), Genetic Algorithm (GA), Particle Swarm Optimi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305325000547 |
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| Summary: | Abstracts: This research investigates the optimization of hyperparameters in Convolutional Neural Networks (CNNs) to enhance the performance of Network Intrusion Detection Systems (NIDS). Four distinct optimization techniques, including Grid Search (GS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), are rigorously examined. Comprehensive experiments employ the UNSW-NB15 and CSE-CIC-IDS2018 datasets to determine the optimal hyperparameter configurations for each technique. Subsequently, detailed experiments evaluate the efficiency of the optimized models in detecting network attacks.The findings consistently reveal that optimized CNN models outperform their non-optimized counterparts across all optimization techniques. Notably, GWO emerges as the top-performing technique, achieving remarkable detection performance on both datasets, with 97.08 % accuracy, 96.95 % precision, 97.21 % recall, and 97.08 % F1 score on the UNSW-NB15 dataset, and 96.37 % accuracy, 96.13 % precision, 96.59 % recall, and 96.36 % F1 score on the CSE-CIC-IDS2018 dataset. Furthermore, hyperparameter optimization significantly reduces training and testing times. The GWO-optimized model achieved a reduction of >11 % in training time and 6.14 % in testing time on the UNSW-NB15 dataset. On the CSE-CIC-IDS2018 dataset, the GA-optimized model provided the best improvements, reducing training and testing times by 9.63 % and 8.61 %, respectively.In comparison to existing CNN models trained on the same datasets, the GWO-optimized CNN model consistently excels in all performance metrics, without the need for complex hybrid Deep Learning models. These results underscore the value of systematic hyperparameter optimization in enhancing CNN-based NIDS, with GWO standing out as a compelling technique for achieving optimal model configurations. |
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| ISSN: | 2667-3053 |