A novel hyperparameter tuning method for enhanced intrusion detection in network security

Turkish Journal of Engineering, Journal Year: 2025, Volume and Issue: 9(3), P. 519 - 534

Published: March 9, 2025

Intrusion Detection Systems (IDS) are essential for ensuring the security of enterprise networks and cloud-based systems, as they defend against sophisticated evolving cyberattacks. Machine learning (ML) techniques have emerged effective tools to enhance IDS performance, addressing limitations traditional methods. This study proposes a novel hyperparameter tuning method ML-based IDS, leveraging NSL-KDD dataset with extensive feature selection preprocessing address data imbalance redundancy. The method, integrating adaptive refinement stochastic perturbation, optimizes classifiers such Random Forest (RF), Gradient Boosting (GB), Extreme (XGB), achieving both higher detection accuracy (99.90% RF) improved computational efficiency. approach excels due its dynamic adjustment parameter ranges controlled randomness, converging faster than Grid Search by reducing iterations up 87.5%. experimental results demonstrate that tree-based models, particularly RF, outperform others their ability model complex, non-linear patterns, enhanced proposed method. Measured in terms convergence speed, CPU time, memory usage, this proves suitable deployment real-time, resource-constrained environments, offering scalable efficient solution network security.

Language: Английский

---

AIpollen: An Analytic Website for Pollen Identification Through Convolutional Neural Networks

Plants, Journal Year: 2024, Volume and Issue: 13(22), P. 3118 - 3118

Published: Nov. 5, 2024

With the rapid development of artificial intelligence, deep learning has been widely applied to complex tasks such as computer vision and natural language processing, demonstrating its outstanding performance. This study aims exploit high precision efficiency develop a system for identification pollen. To this end, we constructed dataset across 36 distinct genera. In terms model selection, employed pre-trained ResNet34 network fine-tuned architecture suit our specific task. For optimization algorithm, opted Adam optimizer utilized cross-entropy loss function. Additionally, implemented ELU activation function, data augmentation, rate decay, early stopping strategies enhance training generalization capability model. After 203 epochs, achieved an accuracy 97.01% on test set 99.89% set. Further evaluation metrics, F1 score 95.9%, indicate that exhibits good balance robustness all categories. facilitate use model, user-friendly web interface. Users can upload images pollen grains through URL link provided in article) immediately receive predicted results their genus names. Altogether, successfully trained validated high-precision grain providing powerful tool

Language: Английский

---

Imbalanced data learning using SMOTE and deep learning architecture with optimized features

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 12, 2024

Language: Английский

---