Networks Intrusion Detection Using Optimized Hybrid Network

Selvarajan, Padmanayaki; Salman, Rahama; Ahamed, Saahira Banu; Jayasuriya, P.

doi:10.1109/icsca57840.2023.10087611

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Hyperparameters directly regulate the performances of DL algorithms and significantly influence the model detection performance [5]. Therefore, the selection of hyperparameters plays a vital role in the successful execution of DL projects.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, managing hyperparameters and mitigating the need for substantial computational resources are crucial steps. To this end, the authors of [5] selected optimal hyperparameters for their proposed hybrid models by testing various hyperparameters and rigorously training each model. They proposed a comprehensive approach to hyperparameter optimization, emphasizing the importance of this step in model development and training.…”

Section: Related Workmentioning

confidence: 99%

“…Hyperparameters, the parameters not learned from data but rather set before training a model, play a pivotal role in determining a model's performance [4]. While manual tuning might seem like a logical step [5], it is akin to searching for a needle in a haystack given the extensive hyperparameter space and variances across datasets. Therefore, efficient hyperparameter-optimization techniques are required to improve the accuracy and efficiency of NIDSs [6].…”

Section: Introduction 1research Context and Motivationmentioning

confidence: 99%

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Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO

Kilichev,

Kim

2023

Mathematics

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This study presents a comprehensive exploration of the hyperparameter optimization in one-dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The increasing frequency and complexity of cyberattacks have prompted an urgent need for effective intrusion-detection systems (IDSs). Herein, we focus on optimizing nine hyperparameters within a 1D-CNN model, using two well-established evolutionary computation methods—genetic algorithm (GA) and particle swarm optimization (PSO). The performances of these methods are assessed using three major datasets—UNSW-NB15, CIC-IDS2017, and NSL-KDD. The key performance metrics considered in this study include the accuracy, loss, precision, recall, and F1-score. The results demonstrate considerable improvements in all metrics across all datasets, for both GA- and PSO-optimized models, when compared to those of the original nonoptimized 1D-CNN model. For instance, on the UNSW-NB15 dataset, GA and PSO achieve accuracies of 99.31 and 99.28%, respectively. Both algorithms yield equivalent results in terms of the precision, recall, and F1-score. Similarly, the performances of GA and PSO vary on the CIC-IDS2017 and NSL-KDD datasets, indicating that the efficacy of the optimization algorithm is context-specific and dependent on the nature of the dataset. The findings of this study demonstrate the importance and effects of efficient hyperparameter optimization, greatly contributing to the field of network security. This study serves as a crucial step toward developing advanced, robust, and adaptable IDSs capable of addressing the evolving landscape of cyber threats.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%